NV-Bench: Benchmark of Nonverbal Vocalization Synthesis for Expressive Text-to-Speech Generation
Abstract
While recent text-to-speech (TTS) systems increasingly integrate nonverbal vocalizations (NVs), their evaluations lack standardized metrics and reliable ground-truth references. To bridge this gap, we propose NV-Bench, the first benchmark grounded in a functional taxonomy that treats NVs as communicative acts rather than acoustic artifacts. NV-Bench comprises 1,651 multi-lingual, in-the-wild utterances with paired human reference audio, balanced across 14 NV categories. We introduce a dual-dimensional evaluation protocol: (1) Instruction Alignment, utilizing the proposed paralinguistic character error rate (PCER) to assess controllability, (2) Acoustic Fidelity, measuring the distributional gap to real recordings to assess acoustic realism. We evaluate diverse TTS models and develop two baselines. Experimental results demonstrate a strong correlation between our objective metrics and human perception, establishing NV-Bench as a standardized evaluation framework.
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 NV-Bench, a pioneering benchmark for nonverbal vocalization synthesis in expressive TTS generation, establishing a standardized evaluation framework that enhances the assessment of TTS models. The comprehensive methodology, extensive experimental evaluation, and potential for broader impact underscore its significance in advancing the field of machine learning and audio synthesis.
Methodology
The methodology employed in NV-Bench is robust and well-structured, focusing on the creation of a comprehensive benchmark for nonverbal vocalization synthesis in TTS systems. The authors introduce a dual-dimensional evaluation protocol that effectively separates instruction alignment and acoustic fidelity, which is a significant advancement over existing methods that often conflate these aspects. The use of a functional taxonomy to categorize NVs as communicative acts adds depth to the evaluation process, allowing for a more nuanced understanding of NVs in TTS systems. The data collection process is thorough, utilizing a multi-lingual NVASR model and a rigorous filtering pipeline that ensures high-quality, human-verified ground truth data. This attention to detail in methodology enhances the credibility of the benchmark.
Results & evaluation
The experimental evaluation is comprehensive, involving a diverse set of TTS models and a well-balanced dataset of 1,651 utterances across 14 NV categories. The results demonstrate a strong correlation between the proposed objective metrics and human perception, validating the effectiveness of NV-Bench as a standardized evaluation framework. The benchmarking of state-of-the-art TTS models provides valuable insights into their performance regarding controllability and acoustic fidelity, highlighting the practical utility of the proposed framework. The inclusion of both single-label and multi-label subsets allows for a thorough assessment of model capabilities under varying conditions.
Reproducibility
The paper provides sufficient details regarding the implementation of the NVASR model and the data collection process, which aids in reproducibility. However, the lack of a publicly available code repository may hinder full reproducibility for some researchers. The authors could enhance reproducibility by providing access to the models and datasets used in their experiments, as well as detailed training configurations.
Limitations
One limitation of the study is the potential bias introduced by the selection of the dataset, which is curated from online audiovisual media. While efforts are made to ensure diversity and balance, the long-tail distribution of NV events may still lead to underrepresentation of certain NV categories. Additionally, the reliance on human verification for ground truth may introduce variability based on annotator subjectivity, which could affect the consistency of the evaluation metrics.
Broader impact
The introduction of NV-Bench has the potential to significantly impact the field of text-to-speech synthesis by providing a standardized framework for evaluating NVs, which are crucial for enhancing the expressiveness of TTS systems. This benchmark can facilitate fair comparisons among different TTS models, driving advancements in the development of more human-like speech synthesis. The implications extend beyond academic research, as improved TTS systems can enhance applications in virtual assistants, gaming, and other interactive media where expressive communication is essential. The paper presents NV-Bench, a pioneering benchmark for nonverbal vocalization synthesis in expressive TTS generation, establishing a standardized evaluation framework that enhances the assessment of TTS models. The comprehensive methodology, extensive experimental evaluation, and potential for broader impact underscore its significance in advancing the field of machine learning and audio synthesis.
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