Audio Question Answering (AQA) is a key task for evaluating Audio-Language Models (ALMs), yet assessing open-ended responses remains challenging. Existing metrics used for AQA such as BLEU, METEOR and BERTScore, mostly adapted from NLP and audio captioning, rely on surface similarity and fail to account for question context, reasoning, and partial correctness. To address the gap in literature, we make three contributions in this work. First, we introduce AQEval to enable systematic benchmarking of AQA metrics. It is the first benchmark of its kind, consisting of 10k model responses annotated by multiple humans for their correctness and relevance. Second, we conduct a comprehensive analysis of existing AQA metrics on AQEval, highlighting weak correlation with human judgment, especially for longer answers. Third, we propose a new metric - AURA score, to better evaluate open-ended model responses. On AQEval, AURA achieves state-of-the-art correlation with human ratings, significantly outperforming all baselines. Through this work, we aim to highlight the limitations of current AQA evaluation methods and motivate better metrics. We release both the AQEval benchmark and the AURA metric to support future research in holistic AQA evaluation.
Primary: Carnegie Mellon University
All Institutions: Carnegie Mellon University
The main contribution of this paper is the introduction of AQEval and the AURA score, which together provide a comprehensive framework for evaluating open-ended responses in audio question answering. This work addresses critical shortcomings in existing evaluation metrics and sets a new standard for future research in the field.
The paper introduces AQEval, a novel benchmark for Audio Question Answering (AQA) metrics, which is a significant advancement in evaluating open-ended responses in audio contexts. The methodology employs a combination of human annotations and a new metric, AURA, which integrates reasoning capabilities of large language models (LLMs) with an audio entailment component. This dual approach allows for a more nuanced evaluation of responses, addressing the limitations of existing metrics that primarily focus on surface-level similarity.
The experimental setup is robust, utilizing a dataset of 10k annotated responses that allows for systematic benchmarking of AQA metrics. The authors provide a comprehensive analysis of existing metrics, demonstrating their weak correlation with human judgments, particularly for longer answers. AURA is shown to outperform traditional metrics significantly, achieving state-of-the-art correlation with human ratings. The ablation studies further validate the effectiveness of the proposed methodology.
The paper includes detailed descriptions of the dataset construction, annotation process, and experimental setup, which enhances reproducibility. However, the reliance on specific LLMs for scoring may limit the generalizability of the results to other models or contexts.
While the paper addresses significant gaps in AQA evaluation, it does not explore the potential biases in human annotations or the limitations of the LLMs used. Additionally, the performance of AURA in real-world applications remains to be fully validated.
The introduction of AQEval and AURA has the potential to significantly influence future research in audio-language models and their evaluation. By providing a more accurate assessment of model responses, this work can lead to improvements in the development of ALMs and their applications in various domains, including accessibility, education, and content creation. The main contribution of this paper is the introduction of AQEval and the AURA score, which together provide a comprehensive framework for evaluating open-ended responses in audio question answering. This work addresses critical shortcomings in existing evaluation metrics and sets a new standard for future research in the field.
While language models (LMs) paired with residual vector quantization (RVQ) tokenizers have shown promise in text-to-audio (T2A) generation, they still lag behind diffusion-based models by a non-trivial margin. We identify a critical dilemma underpinning this gap: incorporating more RVQ layers improves audio reconstruction fidelity but exceeds the generation capacity of conventional LMs. To address this, we first analyze RVQ dynamics and uncover two key limitations: 1) orthogonality of features across RVQ layers hinders effective LMs training, and 2) descending semantic richness in tokens from deeper RVQ layers exacerbates exposure bias during autoregressive decoding. Based on these insights, we propose Siren, a novel LM-based framework that employs multiple isolated transformers with causal conditioning and anti-causal alignment via reinforcement learning. Extensive experiments demonstrate that Siren outperforms both existing LM-based and diffusion-based T2A systems, achieving state-of-the-art results. By bridging the representational strengths of LMs with the fidelity demands of audio synthesis, our approach repositions LMs as competitive contenders against diffusion models in T2A tasks. Moreover, by aligning audio representations with linguistic structures, Siren facilitates a promising pathway toward unified multi-modal generation frameworks.
Primary: The Hong Kong Polytechnic University
All Institutions: The Hong Kong Polytechnic University, Tsinghua University
The main contribution of this paper is the introduction of the Siren framework, which effectively bridges the gap between language models and diffusion models in text-to-audio generation. The comprehensive analysis of the methodology and experimental results highlights its potential to reshape the landscape of audio synthesis, making it a notable advancement in the field.
The proposed methodology introduces a novel framework, Siren, which utilizes multiple isolated transformers with causal conditioning and anti-causal alignment. This approach effectively addresses the limitations of existing RVQ tokenizers in T2A generation by mitigating gradient conflicts and enhancing audio reconstruction fidelity. The use of reinforcement learning for alignment is innovative, although the complexity of the architecture may pose challenges for implementation and scalability.
The experiments are extensive and demonstrate that Siren outperforms both existing LM-based and diffusion-based systems, achieving state-of-the-art results. However, the paper mentions the use of a curated dataset smaller than those in prior work, which raises questions about the generalizability of the results. The evaluation metrics, particularly in terms of fidelity, are well-defined, but further comparisons with a broader range of benchmarks would strengthen the findings.
The paper provides a GitHub repository link for the implementation, which is crucial for reproducibility. However, details on the training process, hyperparameters, and specific datasets used are somewhat limited, which could hinder replication efforts by other researchers.
The authors acknowledge several limitations, including training efficiency due to the sequential training of transformer modules, the trade-off between model size and semantic richness, and the need for larger, more diverse datasets. Addressing these limitations in future work will be essential for advancing the field.
The work has significant implications for multi-modal generation frameworks, potentially enabling more cohesive integration of audio and text. By repositioning LMs as competitive in T2A tasks, it opens pathways for applications in content creation, gaming, and accessibility technologies. The main contribution of this paper is the introduction of the Siren framework, which effectively bridges the gap between language models and diffusion models in text-to-audio generation. The comprehensive analysis of the methodology and experimental results highlights its potential to reshape the landscape of audio synthesis, making it a notable advancement in the field.
Sound field reconstruction involves estimating sound fields from a limited number of spatially distributed observations. This work introduces a differentiable physics approach for sound field reconstruction, where the initial conditions of the wave equation are approximated with a neural network, and the differential operator is computed with a differentiable numerical solver. The use of a numerical solver enables a stable network training while enforcing the physics as a strong constraint, in contrast to conventional physics-informed neural networks, which include the physics as a constraint in the loss function. We introduce an additional sparsity-promoting constraint to achieve meaningful solutions even under severe undersampling conditions. Experiments demonstrate that the proposed approach can reconstruct sound fields under extreme data scarcity, achieving higher accuracy and better convergence compared to physics-informed neural networks.
Primary: Technical University of Denmark (DTU)
All Institutions: Technical University of Denmark (DTU), Universidad Politécnica de Madrid (UPM)
This work introduces a differentiable physics approach for sound field reconstruction, significantly enhancing accuracy and convergence under data scarcity. The methodology combines neural networks with numerical PDE solvers, showcasing a promising direction for future research in acoustics and machine learning.
The paper presents a novel differentiable physics approach that integrates a neural network with a numerical PDE solver for sound field reconstruction. This method improves stability and convergence compared to traditional physics-informed neural networks (PINNs) by directly incorporating physical constraints through the numerical solver rather than as a penalty in the loss function. The introduction of a sparsity-promoting constraint is particularly innovative, allowing the model to perform well under extreme data scarcity. The use of automatic differentiation (AD) to compute gradients through the numerical solver is a significant methodological advancement, streamlining the training process.
The experiments conducted are rigorous and demonstrate the effectiveness of the proposed method across various scenarios, including single Gaussian pulses and complex source distributions. The results indicate that the differentiable physics approach significantly outperforms PINNs in terms of accuracy and convergence speed, particularly in highly undersampled conditions. The use of normalized mean squared error (NMSE) as a performance metric is appropriate, and the experiments are well-structured to showcase the strengths of the proposed method.
The paper provides sufficient detail regarding the implementation, including the architecture of the neural networks, the training process, and the numerical methods used. The availability of the code repository enhances reproducibility, allowing other researchers to replicate the experiments and build upon the work.
While the proposed method shows promising results, the paper does not extensively discuss potential limitations, such as the sensitivity of the model to the choice of hyperparameters or the specific numerical methods employed. Additionally, the method may face challenges in more complex acoustic environments that were not tested in the experiments.
The proposed differentiable physics approach has significant implications for sound field reconstruction and could be applied to various fields, including acoustics, audio engineering, and environmental monitoring. The ability to reconstruct sound fields from limited data could enhance applications in virtual reality, architectural acoustics, and audio signal processing. The integration of physics with machine learning also opens avenues for addressing other inverse problems in different domains. This work introduces a differentiable physics approach for sound field reconstruction, significantly enhancing accuracy and convergence under data scarcity. The methodology combines neural networks with numerical PDE solvers, showcasing a promising direction for future research in acoustics and machine learning.
Audio Question Answering (AQA) is a key task for evaluating Audio-Language Models (ALMs), yet assessing open-ended responses remains challenging. Existing metrics used for AQA such as BLEU, METEOR and BERTScore, mostly adapted from NLP and audio captioning, rely on surface similarity and fail to account for question context, reasoning, and partial correctness. To address the gap in literature, we make three contributions in this work. First, we introduce AQEval to enable systematic benchmarking of AQA metrics. It is the first benchmark of its kind, consisting of 10k model responses annotated by multiple humans for their correctness and relevance. Second, we conduct a comprehensive analysis of existing AQA metrics on AQEval, highlighting weak correlation with human judgment, especially for longer answers. Third, we propose a new metric - AURA score, to better evaluate open-ended model responses. On AQEval, AURA achieves state-of-the-art correlation with human ratings, significantly outperforming all baselines. Through this work, we aim to highlight the limitations of current AQA evaluation methods and motivate better metrics. We release both the AQEval benchmark and the AURA metric to support future research in holistic AQA evaluation.
Primary: Carnegie Mellon University
All Institutions: Carnegie Mellon University
The main contribution of this paper is the introduction of AQEval and the AURA score, which together provide a comprehensive framework for evaluating open-ended responses in audio question answering. This work addresses critical shortcomings in existing evaluation metrics and sets a new standard for future research in the field.
The paper introduces AQEval, a novel benchmark for Audio Question Answering (AQA) metrics, which is a significant advancement in evaluating open-ended responses in audio contexts. The methodology employs a combination of human annotations and a new metric, AURA, which integrates reasoning capabilities of large language models (LLMs) with an audio entailment component. This dual approach allows for a more nuanced evaluation of responses, addressing the limitations of existing metrics that primarily focus on surface-level similarity.
The experimental setup is robust, utilizing a dataset of 10k annotated responses that allows for systematic benchmarking of AQA metrics. The authors provide a comprehensive analysis of existing metrics, demonstrating their weak correlation with human judgments, particularly for longer answers. AURA is shown to outperform traditional metrics significantly, achieving state-of-the-art correlation with human ratings. The ablation studies further validate the effectiveness of the proposed methodology.
The paper includes detailed descriptions of the dataset construction, annotation process, and experimental setup, which enhances reproducibility. However, the reliance on specific LLMs for scoring may limit the generalizability of the results to other models or contexts.
While the paper addresses significant gaps in AQA evaluation, it does not explore the potential biases in human annotations or the limitations of the LLMs used. Additionally, the performance of AURA in real-world applications remains to be fully validated.
The introduction of AQEval and AURA has the potential to significantly influence future research in audio-language models and their evaluation. By providing a more accurate assessment of model responses, this work can lead to improvements in the development of ALMs and their applications in various domains, including accessibility, education, and content creation. The main contribution of this paper is the introduction of AQEval and the AURA score, which together provide a comprehensive framework for evaluating open-ended responses in audio question answering. This work addresses critical shortcomings in existing evaluation metrics and sets a new standard for future research in the field.
Sound field reconstruction involves estimating sound fields from a limited number of spatially distributed observations. This work introduces a differentiable physics approach for sound field reconstruction, where the initial conditions of the wave equation are approximated with a neural network, and the differential operator is computed with a differentiable numerical solver. The use of a numerical solver enables a stable network training while enforcing the physics as a strong constraint, in contrast to conventional physics-informed neural networks, which include the physics as a constraint in the loss function. We introduce an additional sparsity-promoting constraint to achieve meaningful solutions even under severe undersampling conditions. Experiments demonstrate that the proposed approach can reconstruct sound fields under extreme data scarcity, achieving higher accuracy and better convergence compared to physics-informed neural networks.
Primary: Technical University of Denmark (DTU)
All Institutions: Technical University of Denmark (DTU), Universidad Politécnica de Madrid (UPM)
This work introduces a differentiable physics approach for sound field reconstruction, significantly enhancing accuracy and convergence under data scarcity. The methodology combines neural networks with numerical PDE solvers, showcasing a promising direction for future research in acoustics and machine learning.
The paper presents a novel differentiable physics approach that integrates a neural network with a numerical PDE solver for sound field reconstruction. This method improves stability and convergence compared to traditional physics-informed neural networks (PINNs) by directly incorporating physical constraints through the numerical solver rather than as a penalty in the loss function. The introduction of a sparsity-promoting constraint is particularly innovative, allowing the model to perform well under extreme data scarcity. The use of automatic differentiation (AD) to compute gradients through the numerical solver is a significant methodological advancement, streamlining the training process.
The experiments conducted are rigorous and demonstrate the effectiveness of the proposed method across various scenarios, including single Gaussian pulses and complex source distributions. The results indicate that the differentiable physics approach significantly outperforms PINNs in terms of accuracy and convergence speed, particularly in highly undersampled conditions. The use of normalized mean squared error (NMSE) as a performance metric is appropriate, and the experiments are well-structured to showcase the strengths of the proposed method.
The paper provides sufficient detail regarding the implementation, including the architecture of the neural networks, the training process, and the numerical methods used. The availability of the code repository enhances reproducibility, allowing other researchers to replicate the experiments and build upon the work.
While the proposed method shows promising results, the paper does not extensively discuss potential limitations, such as the sensitivity of the model to the choice of hyperparameters or the specific numerical methods employed. Additionally, the method may face challenges in more complex acoustic environments that were not tested in the experiments.
The proposed differentiable physics approach has significant implications for sound field reconstruction and could be applied to various fields, including acoustics, audio engineering, and environmental monitoring. The ability to reconstruct sound fields from limited data could enhance applications in virtual reality, architectural acoustics, and audio signal processing. The integration of physics with machine learning also opens avenues for addressing other inverse problems in different domains. This work introduces a differentiable physics approach for sound field reconstruction, significantly enhancing accuracy and convergence under data scarcity. The methodology combines neural networks with numerical PDE solvers, showcasing a promising direction for future research in acoustics and machine learning.
While language models (LMs) paired with residual vector quantization (RVQ) tokenizers have shown promise in text-to-audio (T2A) generation, they still lag behind diffusion-based models by a non-trivial margin. We identify a critical dilemma underpinning this gap: incorporating more RVQ layers improves audio reconstruction fidelity but exceeds the generation capacity of conventional LMs. To address this, we first analyze RVQ dynamics and uncover two key limitations: 1) orthogonality of features across RVQ layers hinders effective LMs training, and 2) descending semantic richness in tokens from deeper RVQ layers exacerbates exposure bias during autoregressive decoding. Based on these insights, we propose Siren, a novel LM-based framework that employs multiple isolated transformers with causal conditioning and anti-causal alignment via reinforcement learning. Extensive experiments demonstrate that Siren outperforms both existing LM-based and diffusion-based T2A systems, achieving state-of-the-art results. By bridging the representational strengths of LMs with the fidelity demands of audio synthesis, our approach repositions LMs as competitive contenders against diffusion models in T2A tasks. Moreover, by aligning audio representations with linguistic structures, Siren facilitates a promising pathway toward unified multi-modal generation frameworks.
Primary: The Hong Kong Polytechnic University
All Institutions: The Hong Kong Polytechnic University, Tsinghua University
The main contribution of this paper is the introduction of the Siren framework, which effectively bridges the gap between language models and diffusion models in text-to-audio generation. The comprehensive analysis of the methodology and experimental results highlights its potential to reshape the landscape of audio synthesis, making it a notable advancement in the field.
The proposed methodology introduces a novel framework, Siren, which utilizes multiple isolated transformers with causal conditioning and anti-causal alignment. This approach effectively addresses the limitations of existing RVQ tokenizers in T2A generation by mitigating gradient conflicts and enhancing audio reconstruction fidelity. The use of reinforcement learning for alignment is innovative, although the complexity of the architecture may pose challenges for implementation and scalability.
The experiments are extensive and demonstrate that Siren outperforms both existing LM-based and diffusion-based systems, achieving state-of-the-art results. However, the paper mentions the use of a curated dataset smaller than those in prior work, which raises questions about the generalizability of the results. The evaluation metrics, particularly in terms of fidelity, are well-defined, but further comparisons with a broader range of benchmarks would strengthen the findings.
The paper provides a GitHub repository link for the implementation, which is crucial for reproducibility. However, details on the training process, hyperparameters, and specific datasets used are somewhat limited, which could hinder replication efforts by other researchers.
The authors acknowledge several limitations, including training efficiency due to the sequential training of transformer modules, the trade-off between model size and semantic richness, and the need for larger, more diverse datasets. Addressing these limitations in future work will be essential for advancing the field.
The work has significant implications for multi-modal generation frameworks, potentially enabling more cohesive integration of audio and text. By repositioning LMs as competitive in T2A tasks, it opens pathways for applications in content creation, gaming, and accessibility technologies. The main contribution of this paper is the introduction of the Siren framework, which effectively bridges the gap between language models and diffusion models in text-to-audio generation. The comprehensive analysis of the methodology and experimental results highlights its potential to reshape the landscape of audio synthesis, making it a notable advancement in the field.
We introduce MAVE (Mamba with Cross-Attention for Voice Editing and Synthesis), a novel autoregressive architecture for text-conditioned voice editing and high-fidelity text-to-speech (TTS) synthesis, built on a cross-attentive Mamba backbone. MAVE achieves state-of-the-art performance in speech editing and very competitive results in zero-shot TTS, while not being explicitly trained on the latter task, outperforming leading autoregressive and diffusion models on diverse, real-world audio. By integrating Mamba for efficient audio sequence modeling with cross-attention for precise text-acoustic alignment, MAVE enables context-aware voice editing with exceptional naturalness and speaker consistency. In pairwise human evaluations on a random 40-sample subset of the RealEdit benchmark (400 judgments), 57.2% of listeners rated MAVE - edited speech as perceptually equal to the original, while 24.8% prefered the original and 18.0% MAVE - demonstrating that in the majority of cases edits are indistinguishable from the source. MAVE compares favorably with VoiceCraft and FluentSpeech both on pairwise comparisons and standalone mean opinion score (MOS) evaluations. For zero-shot TTS, MAVE exceeds VoiceCraft in both speaker similarity and naturalness, without requiring multiple inference runs or post-processing. Remarkably, these quality gains come with a significantly lower memory cost and approximately the same latency: MAVE requires ~6x less memory than VoiceCraft during inference on utterances from the RealEdit database (mean duration: 6.21s, A100, FP16, batch size 1). Our results demonstrate that MAVE establishes a new standard for flexible, high-fidelity voice editing and synthesis through the synergistic integration of structured state-space modeling and cross-modal attention.
Primary: unknown
All Institutions: unknown
MAVE establishes a new standard for flexible, high-fidelity voice editing and synthesis through the integration of structured state-space modeling and cross-modal attention. The paper presents a compelling advancement in the field of audio processing, demonstrating significant improvements in both efficiency and quality, although it would benefit from enhanced reproducibility measures and a discussion of ethical implications.
The paper presents MAVE, an autoregressive architecture that integrates a cross-attentive mechanism with a Mamba backbone for voice editing and TTS synthesis. The methodology is well-structured, leveraging state-space modeling and cross-modal attention to achieve high fidelity in voice editing and synthesis. The use of cross-attention for text-acoustic alignment is particularly innovative, allowing for context-aware modifications to audio. The autoregressive nature of the model also suggests a thoughtful approach to sequence generation, although details on the training regimen and hyperparameter tuning could enhance the understanding of the model's performance.
The experiments are robust, utilizing a variety of benchmarks, including the RealEdit dataset, to evaluate the model's performance. The human evaluation metrics, including pairwise comparisons and MOS scores, provide a comprehensive view of the model's effectiveness. The results indicate that MAVE not only matches but often exceeds existing models like VoiceCraft and FluentSpeech, particularly in terms of memory efficiency and naturalness. However, the paper could benefit from a more detailed analysis of the datasets used and the statistical significance of the results.
The paper lacks sufficient detail regarding the implementation specifics, such as the training process, data preprocessing, and evaluation metrics, which are crucial for reproducibility. While the architecture is described, the absence of code or a demo URL limits the ability for other researchers to replicate the findings. Including a link to a GitHub repository or supplementary materials would significantly enhance reproducibility.
One limitation is the reliance on subjective human evaluations, which can introduce variability and bias. Additionally, while the model shows promise in zero-shot TTS, the performance on diverse speaker characteristics and accents remains unexplored. The paper does not address potential ethical concerns related to voice synthesis technology, such as misuse in deepfakes or privacy violations.
The implications of MAVE are significant, particularly in applications like voice dubbing, personalized voice assistants, and content creation. The ability to edit voice recordings seamlessly has the potential to revolutionize industries reliant on audio content. However, the technology also raises ethical questions regarding consent and the potential for misuse, necessitating careful consideration in its deployment. MAVE establishes a new standard for flexible, high-fidelity voice editing and synthesis through the integration of structured state-space modeling and cross-modal attention. The paper presents a compelling advancement in the field of audio processing, demonstrating significant improvements in both efficiency and quality, although it would benefit from enhanced reproducibility measures and a discussion of ethical implications.
Large language models (LLMs) have demonstrated promising performance in both automatic speech recognition (ASR) and text-to-speech (TTS) systems, gradually becoming the mainstream approach. However, most current approaches address these tasks separately rather than through a unified framework. This work aims to integrate these two tasks into one unified model. Although discrete speech tokenization enables joint modeling, its inherent information loss limits performance in both recognition and generation. In this work, we present UniVoice, a unified LLM framework through continuous representations that seamlessly integrates speech recognition and synthesis within a single model. Our approach combines the strengths of autoregressive modeling for speech recognition with flow matching for high-quality generation. To mitigate the inherent divergence between autoregressive and flow-matching models, we further design a dual attention mechanism, which switches between a causal mask for recognition and a bidirectional attention mask for synthesis. Furthermore, the proposed text-prefix-conditioned speech infilling method enables high-fidelity zero-shot voice cloning. Experimental results demonstrate that our method can achieve or exceed current single-task modeling methods in both ASR and zero-shot TTS tasks. This work explores new possibilities for end-to-end speech understanding and generation.
Primary: unknown
All Institutions: unknown
The paper presents UniVoice, a unified transformer framework that integrates autoregressive speech recognition with flow-matching-based speech synthesis. This work is significant as it explores a novel approach to joint modeling in speech processing, addressing critical limitations in current methodologies and demonstrating robust performance across multiple tasks.
The methodology presented in this paper is innovative, as it proposes a unified framework that integrates autoregressive speech recognition with flow-matching-based synthesis. The dual attention mechanism and text-prefix-guided speech infilling method are significant contributions that address the limitations of existing models that treat ASR and TTS as separate tasks. The continuous representation approach is a notable departure from traditional discrete tokenization methods, which often suffer from information loss. The paper also provides a clear description of the model architecture, training objectives, and attention mask design, which enhances the understanding of the proposed methods.
The experimental evaluation is thorough, utilizing the LibriHeavy dataset for both ASR and TTS tasks. The results demonstrate that UniVoice achieves competitive performance compared to state-of-the-art models in both domains, with specific metrics provided for robustness, similarity, and quality. The ablation studies effectively showcase the advantages of the proposed methods over baseline models, although the paper acknowledges trade-offs in performance when compared to specialized models.
The paper provides sufficient implementation details, including model architecture, training procedures, and evaluation metrics, which supports reproducibility. The authors also mention plans to open-source the code and checkpoints, which is a positive step towards enabling other researchers to replicate and build upon their work.
The paper identifies several limitations, including the focus on only ASR and TTS tasks, the relatively small dataset and model size, and the underutilization of the conversational capabilities of LLMs. These limitations suggest that while the work is a significant step forward, there is potential for further development and exploration in future research.
The unified framework proposed in this paper has the potential to advance the field of speech processing by enabling more efficient and effective models that can handle both recognition and synthesis tasks. This could lead to improvements in applications such as virtual assistants, automated transcription services, and voice cloning technologies, ultimately enhancing user experience and accessibility in various domains. The paper presents UniVoice, a unified transformer framework that integrates autoregressive speech recognition with flow-matching-based speech synthesis. This work is significant as it explores a novel approach to joint modeling in speech processing, addressing critical limitations in current methodologies and demonstrating robust performance across multiple tasks.