Improving Automatic Speech Recognition Accuracy for People Who Stutter Through Fine-Tuning and Data Augmentation

核心概念

Developing an accessible automatic speech recognition (ASR) system that can accurately process speech from individuals who stutter by leveraging fine-tuning on a curated dataset and a novel data augmentation technique to enrich the training data with diverse disfluency patterns.

摘要

The paper presents an approach to improve the accuracy of automatic speech recognition (ASR) systems for processing speech from individuals who stutter. The key contributions are:

ASR fine-tuning for accessibility: The authors investigate the impact of fine-tuning a pre-trained wav2vec 2.0 model on a dataset of stuttered speech to enhance the model's performance on disfluent speech.

Disfluent speech data augmentation: An innovative data augmentation method is introduced to overcome the challenge of limited data on stuttered speech. This method allows for precise control over the types, frequency, and placement of disfluencies within speech samples, enabling the enrichment of the training dataset.

Accuracy bias analysis: The effectiveness of the proposed approach in mitigating the accuracy bias, i.e., the discrepancy in ASR performance between stuttered and non-stuttered speech, is evaluated.

Diverse and realistic evaluation settings: The fine-tuned ASR is assessed using speech from diverse contexts, including interview and reading videos, and across various demographics of people who stutter.

The results show that fine-tuning wav2vec 2.0 with even a relatively small, labeled dataset of stuttered speech, alongside data augmentation, can significantly reduce word error rates for disfluent speech. This approach not only advances the inclusivity of ASR for people who stutter but also paves the way for ASRs that can accommodate a wider range of speech variations.

統計資料

The FluencyBank dataset used in the study consists of 1373 utterances and 2.21 hours of audio duration from 12 participants who stutter.
The authors generated a modified version of the FluencyBank dataset, termed FluencyBank-N, from which all apparent disfluencies have been removed using a text-to-speech model.
The data augmentation technique introduced in the study generates up to 6,000 additional samples with various types of disfluencies, including word repetitions, phrase repetitions, and interjections.

引述

"A critical barrier to progress is the scarcity of large, annotated disfluent speech datasets."
"Consequently, the disparity in ASR accuracy will exacerbate the marginalization of people who stutter, impacting all downstream applications."
"Our data augmentation technique enriches training datasets with various disfluencies, enhancing ASR processing of these speech patterns."

從以下內容提煉的關鍵洞見

Inclusive ASR for Disfluent Speech: Cascaded Large-Scale Self-Supervised Learning with Targeted Fine-Tuning and Data Augmentation

by Dena Mujtaba... 於 arxiv.org 10-03-2024

https://arxiv.org/pdf/2406.10177.pdf

深入探究

How can the data augmentation approach be extended to incorporate a wider range of disfluency types and patterns observed in the speech of individuals who stutter?

To extend the data augmentation approach for automatic speech recognition (ASR) systems, it is essential to incorporate a broader spectrum of disfluency types and patterns that reflect the diverse experiences of individuals who stutter. This can be achieved through several strategies:

Inclusion of Additional Disfluency Types: Beyond word repetitions, phrase repetitions, and interjections, the augmentation method can be expanded to include other disfluency types such as prolongations (e.g., extending sounds), blocks (e.g., pauses where speech is halted), and revisions (e.g., correcting oneself mid-sentence). By analyzing existing datasets and conducting qualitative research with individuals who stutter, researchers can identify and categorize these additional disfluency types.

Variability in Disfluency Patterns: The augmentation process can simulate the variability in disfluency patterns by introducing randomness in the frequency, duration, and placement of disfluencies. For instance, different utterances can have varying lengths of blocks or different numbers of repetitions, reflecting the natural inconsistency found in stuttering.

Contextual Disfluency Simulation: Incorporating contextual factors that influence disfluency, such as emotional state, situational pressure, or conversational context, can enhance the realism of the augmented data. This could involve creating scenarios where disfluencies are more likely to occur, such as during high-stress situations or in informal conversations.

User-Generated Data: Engaging individuals who stutter in the data generation process can provide authentic examples of disfluent speech. Crowdsourcing recordings from diverse speakers can help capture a wide range of disfluency types and patterns, ensuring that the augmented dataset is representative of real-world speech.

Machine Learning Techniques: Advanced machine learning techniques, such as generative adversarial networks (GANs), can be employed to create synthetic speech samples that incorporate complex disfluency patterns. These models can learn from existing speech data to generate new samples that mimic the variability and nuances of stuttered speech.

By implementing these strategies, the data augmentation approach can be significantly enhanced, leading to more robust ASR systems that better accommodate the diverse speech patterns of individuals who stutter.

What other techniques, beyond fine-tuning and data augmentation, could be explored to further improve the inclusivity and robustness of ASR systems for diverse speech variations?

In addition to fine-tuning and data augmentation, several other techniques can be explored to enhance the inclusivity and robustness of ASR systems for diverse speech variations:

Transfer Learning: Utilizing transfer learning from models trained on diverse speech datasets can help improve ASR performance on disfluent speech. By leveraging knowledge from models that have been exposed to various speech patterns, ASR systems can better generalize to the unique characteristics of stuttered speech.

Multi-Task Learning: Implementing multi-task learning frameworks can allow ASR systems to simultaneously learn disfluency detection and speech recognition. By training models to recognize and classify disfluencies while also transcribing speech, the system can become more adept at handling disfluent speech.

Adaptive Learning: Developing adaptive learning algorithms that can adjust to individual speech patterns over time can enhance ASR performance. By continuously learning from user interactions, the system can become more personalized and effective in recognizing the specific disfluencies of individual users.

Incorporating Linguistic Features: Integrating linguistic features, such as prosody and intonation, into the ASR model can improve its ability to understand the context and meaning of disfluent speech. This can help the system differentiate between disfluencies that are part of normal speech and those that indicate stuttering.

User Feedback Mechanisms: Implementing user feedback mechanisms can allow individuals who stutter to provide input on the accuracy of transcriptions. This feedback can be used to refine the ASR model, ensuring that it aligns more closely with the speech patterns and preferences of users.

Collaborative Design: Engaging individuals who stutter in the design and testing phases of ASR systems can provide valuable insights into their needs and preferences. Collaborative design approaches can lead to more user-centered solutions that address the specific challenges faced by this population.

By exploring these techniques, researchers and developers can create more inclusive and robust ASR systems that effectively accommodate a wide range of speech variations, ultimately improving accessibility for individuals who stutter.

What are the potential implications of developing inclusive ASR systems for people who stutter in terms of improving their access to and participation in various domains, such as education, employment, and social interactions?

The development of inclusive ASR systems for individuals who stutter has significant implications for enhancing their access to and participation in various domains, including education, employment, and social interactions:

Improved Communication in Education: Inclusive ASR systems can facilitate better communication for students who stutter, allowing them to participate more fully in classroom discussions, presentations, and online learning environments. By providing accurate transcriptions and voice recognition, these systems can help reduce anxiety and improve confidence, leading to a more equitable educational experience.

Enhanced Employment Opportunities: In the workplace, inclusive ASR technology can empower individuals who stutter to engage more effectively in meetings, interviews, and client interactions. By minimizing the bias against disfluent speech in automated systems, such as job interview scoring algorithms, these technologies can help level the playing field, promoting equal opportunities for employment and career advancement.

Facilitated Social Interactions: ASR systems that accommodate disfluent speech can enhance social interactions for individuals who stutter, making it easier for them to communicate with friends, family, and peers. This can lead to improved social inclusion and reduced feelings of isolation, as individuals feel more comfortable expressing themselves in various social settings.

Reduction of Stigma and Bias: By improving the performance of ASR systems for disfluent speech, there is potential to reduce societal stigma and bias against individuals who stutter. As these technologies become more prevalent and effective, they can contribute to a broader cultural shift towards acceptance and understanding of speech differences.

Increased Participation in Voice-Activated Technologies: As voice-activated technologies become more integrated into daily life, inclusive ASR systems can ensure that individuals who stutter can access and utilize these tools effectively. This can enhance their ability to engage with technology in areas such as home automation, customer service, and personal assistance, promoting greater independence and convenience.

Empowerment through Self-Representation: Inclusive ASR systems can allow individuals who stutter to have their speech patterns accurately represented in transcriptions, fostering a sense of identity and self-acceptance. This empowerment can encourage individuals to embrace their speech differences and advocate for their needs in various contexts.

In summary, the development of inclusive ASR systems has the potential to significantly improve the quality of life for individuals who stutter, enhancing their access to education, employment, and social interactions while promoting a more inclusive society.