CALM: Joint Contextual Acoustic-Linguistic Modeling for Personalization of Multi-Speaker ASR
Abstract
We present CALM, a joint Contextual Acoustic-Linguistic Modeling framework for multi-speaker automatic speech recognition (ASR). In personalized AI scenarios, the joint availability of acoustic and linguistic cues naturally motivates the integration of target-speaker conditioning with contextual biasing in overlapping conversations. CALM implements this integration in an end-to-end framework through speaker embedding-driven target-speaker extraction and dynamic vocabulary-based contextual biasing. We evaluate CALM on simulated English (LibriSpeechMix) and Japanese (Corpus of Spontaneous Japanese mixtures, CSJMix). On two-speaker mixtures, CALM reduces biased word error rate (B-WER) from 12.7 to 4.7 on LibriSpeech2Mix and biased character error rate (B-CER) from 16.6 to 8.4 on CSJMix2 (eval3), demonstrating the effectiveness of joint acoustic-linguistic modeling across languages. We additionally report results on the AMI corpus (IHM-mix condition) to validate performance on standardized speech mixtures.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The paper presents CALM, a pioneering framework that effectively combines acoustic and linguistic cues for improved multi-speaker ASR performance. This comprehensive analysis highlights the framework's innovative methodology, rigorous experimental validation, and potential impact on the field of speech recognition.
Methodology
The proposed CALM framework introduces a novel joint Contextual Acoustic-Linguistic Modeling approach for multi-speaker ASR, integrating target-speaker conditioning with dynamic vocabulary expansion. This end-to-end framework leverages speaker embeddings for target-speaker extraction and contextual biasing, addressing both acoustic and linguistic challenges in overlapping speech scenarios. The methodology is well-structured, employing advanced techniques such as Conformer and Transformer architectures, and includes a comprehensive loss function that combines multiple objectives to enhance performance.
Results & evaluation
The experiments are robust, utilizing multiple datasets (LibriSpeechMix, CSJMix, AMI) to validate the effectiveness of CALM across different languages and conditions. The reported results demonstrate substantial improvements in biased and unbiased word error rates, showcasing the framework's ability to enhance ASR performance in multi-speaker contexts. The use of various biasing list sizes and the detailed analysis of results provide a thorough evaluation of the framework's capabilities.
Reproducibility
The paper provides sufficient implementation details, including architecture specifications, training procedures, and evaluation metrics. However, the lack of a public repository or demo URL limits the ease of reproducibility for external researchers. Clearer guidelines or access to the code would enhance the paper's reproducibility.
Limitations
While CALM shows promising results, the paper acknowledges challenges such as increased insertion errors in conversational datasets like AMI, particularly for short utterances. The reliance on enrollment utterances may also limit practical applications in real-world scenarios where such data may not be readily available. Additionally, the performance degradation observed in certain conditions suggests that further optimization is needed for broader applicability.
Broader impact
The integration of acoustic and linguistic modeling in CALM has significant implications for personalized AI applications, particularly in multi-speaker ASR settings such as meetings and discussions. The advancements made could lead to more accurate transcription services, enhancing accessibility and usability in various domains, including education, business, and healthcare. The paper presents CALM, a pioneering framework that effectively combines acoustic and linguistic cues for improved multi-speaker ASR performance. This comprehensive analysis highlights the framework's innovative methodology, rigorous experimental validation, and potential impact on the field of speech recognition.
Author of this paper? Send a correction.