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Probing the Information Encoded in Neural-based Acoustic Models of Automatic Speech Recognition Systems (2402.19443v1)

Published 29 Feb 2024 in cs.SD, cs.AI, and eess.AS

Abstract: Deep learning architectures have made significant progress in terms of performance in many research areas. The automatic speech recognition (ASR) field has thus benefited from these scientific and technological advances, particularly for acoustic modeling, now integrating deep neural network architectures. However, these performance gains have translated into increased complexity regarding the information learned and conveyed through these black-box architectures. Following many researches in neural networks interpretability, we propose in this article a protocol that aims to determine which and where information is located in an ASR acoustic model (AM). To do so, we propose to evaluate AM performance on a determined set of tasks using intermediate representations (here, at different layer levels). Regarding the performance variation and targeted tasks, we can emit hypothesis about which information is enhanced or perturbed at different architecture steps. Experiments are performed on both speaker verification, acoustic environment classification, gender classification, tempo-distortion detection systems and speech sentiment/emotion identification. Analysis showed that neural-based AMs hold heterogeneous information that seems surprisingly uncorrelated with phoneme recognition, such as emotion, sentiment or speaker identity. The low-level hidden layers globally appears useful for the structuring of information while the upper ones would tend to delete useless information for phoneme recognition.

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Summary

  • The paper introduces a novel protocol to probe hidden layer information in TDNN-F ASR models across diverse speech tasks.
  • It employs a TDNN-F architecture with an ECAPA-TDNN classifier to assess speaker identity, environmental cues, and phoneme recognition capabilities.
  • Findings reveal that lower layers capture environmental noise while higher layers encode speaker gender and speaking rate, challenging conventional ASR models.

Probing the Information Encoded in Neural-based Acoustic Models of ASR Systems

Introduction to the Study

The advancements in deep learning architectures have significantly enhanced the performance of Automatic Speech Recognition (ASR) systems, particularly in acoustic modeling, through the integration of Deep Neural Network (DNN) architectures. Despite these technological strides leading to improvements, understanding what and how information is learned and conveyed by these complex models remains a challenge. This complexity has spurred interest in neural network interpretability within the ASR domain, aiming to demystify the types of information encoded by acoustic models (AMs) and the various layers within these models. This paper proposes a novel protocol to analyze and understand the different natures of information stored in a neural-based AM by examining its performance across a range of speech-related tasks.

Acoustic Model Architecture

The focus falls on a time-delay neural network-based architecture (TDNN-F), trained without speaker adaptation methods to maintain generalizability across speakers. This architectural choice is grounded in its ability to handle highly correlated features by de-correlating non-phonetic information, thus focusing on phoneme recognition. The model training utilized the Librispeech dataset and the Kaldi toolkit, positioning this paper within the context of state-of-the-art ASR systems.

Proposed Protocol

A significant contribution of this research is the introduction of a protocol designed to probe specific information contained within the hidden layers of an AM. By evaluating AM performance on various speech-oriented tasks at different layer levels, the paper aims to reveal the correlations between layer features and task performances. Utilizing an ECAPA-TDNN classifier, this protocol discerns the presence or absence of information such as speaker identity, acoustic environment characteristics, gender, tempo-distortions, and emotional states within the AM's architecture.

Experimentation and Results

The research meticulously engages with five probing tasks: speaker verification, speaking rate detection, speaker gender classification, acoustic environment classification, and speech sentiment/emotion identification. The performance across these tasks—articulated in terms of accuracy or Equal Error Rate (EER)—provides a foundation for analyzing the type of information processed and retained at different layers of the TDNN-F model. For instance, the finding that lower layers are adept at capturing environmental noises, while mid-to-upper layers better encode speaker gender and speaking rate, suggests a nuanced distribution of task-specific information processing within the network. Notably, this multidimensional probing approach challenges the notion that speaker identity information, which is progressively suppressed in upper layers, is necessary for phoneme recognition, aligning with observations in self-supervised models like wav2vec2.

Conclusion and Future Directions

Conclusively, this paper forwards our understanding of ASR systems by providing a nuanced method for dissecting the kind of information encoded by acoustic models at different stages of their architecture. It opens up pathways for future investigations into the vast range of information that AMs potentially encode beyond phoneme recognition, like accents or age, and signals a pivot towards exploring unsupervised representations of the acoustic signal like wav2vec. This work, supported by the French National Research Agency, not only enriches the ASR research landscape but also sets a precedent for future explorations into the interpretability of neural-based systems in speech technology.

Theoretical and Practical Implications

From a theoretical standpoint, this research enriches our understanding of neural-based acoustic models' internal workings, offering insights into the dynamism of information processing that underpins phoneme recognition and beyond. On a practical level, the findings have the potential to inform the development of more nuanced and sophisticated ASR systems capable of leveraging the full spectrum of information contained within speech signals, paving the way for advancements in speech understanding and human-computer interaction.

Speculation on Future Developments in AI

Looking forward, this paper's methodologies and findings could spearhead more focused research into AI's ability to derive complex, multifaceted insights from audio data. By expanding the range of probing tasks and exploring alternative acoustic signal representations, future research could unveil even deeper insights into the potential of ASR systems to decode not just what is being said, but how, by whom, and in what context it is being spoken—ushering in a new era of AI-driven speech technologies.

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