Hey ASR System! Why Aren't You More Inclusive? Automatic Speech Recognition Systems' Bias and Proposed Bias Mitigation Techniques. A Literature Review

• URL: http://arxiv.org/abs/2211.09511v1
• Date: Thu, 17 Nov 2022 13:15:58 GMT
• Title: Hey ASR System! Why Aren't You More Inclusive? Automatic Speech Recognition Systems' Bias and Proposed Bias Mitigation Techniques. A Literature Review
• Authors: Mikel K. Ngueajio and Gloria Washington
• Abstract summary: We present research that addresses ASR biases against gender, race, and the sick and disabled. We also discuss techniques for designing a more accessible and inclusive ASR technology.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Speech is the fundamental means of communication between humans. The advent of AI and sophisticated speech technologies have led to the rapid proliferation of human-to-computer-based interactions, fueled primarily by Automatic Speech Recognition (ASR) systems. ASR systems normally take human speech in the form of audio and convert it into words, but for some users, it cannot decode the speech, and any output text is filled with errors that are incomprehensible to the human reader. These systems do not work equally for everyone and actually hinder the productivity of some users. In this paper, we present research that addresses ASR biases against gender, race, and the sick and disabled, while exploring studies that propose ASR debiasing techniques for mitigating these discriminations. We also discuss techniques for designing a more accessible and inclusive ASR technology. For each approach surveyed, we also provide a summary of the investigation and methods applied, the ASR systems and corpora used, and the research findings, and highlight their strengths and/or weaknesses. Finally, we propose future opportunities for Natural Language Processing researchers to explore in the next level creation of ASR technologies.

Related papers

• Towards Unsupervised Speech Recognition Without Pronunciation Models [57.222729245842054]
  Most languages lack sufficient paired speech and text data to effectively train automatic speech recognition systems. We propose the removal of reliance on a phoneme lexicon to develop unsupervised ASR systems. We experimentally demonstrate that an unsupervised speech recognizer can emerge from joint speech-to-speech and text-to-text masked token-infilling.
  arXiv  Detail & Related papers  (2024-06-12T16:30:58Z)
• Speech Emotion Recognition with ASR Transcripts: A Comprehensive Study on Word Error Rate and Fusion Techniques [17.166092544686553]
  This study benchmarks Speech Emotion Recognition using ASR transcripts with varying Word Error Rates (WERs) on well-known corpora. We propose a unified ASR error-robust framework integrating ASR error correction and modality-gated fusion, achieving lower WER and higher SER results.
  arXiv  Detail & Related papers  (2024-06-12T15:59:25Z)
• A Vector Quantized Approach for Text to Speech Synthesis on Real-World Spontaneous Speech [94.64927912924087]
  We train TTS systems using real-world speech from YouTube and podcasts. Recent Text-to-Speech architecture is designed for multiple code generation and monotonic alignment. We show thatRecent Text-to-Speech architecture outperforms existing TTS systems in several objective and subjective measures.
  arXiv  Detail & Related papers  (2023-02-08T17:34:32Z)
• Speech Aware Dialog System Technology Challenge (DSTC11) [12.841429336655736]
  Most research on task oriented dialog modeling is based on written text input. We created three spoken versions of the popular written-domain MultiWoz task -- (a) TTS-Verbatim: written user inputs were converted into speech waveforms using a TTS system, (b) Human-Verbatim: humans spoke the user inputs verbatim, and (c) Human-paraphrased: humans paraphrased the user inputs.
  arXiv  Detail & Related papers  (2022-12-16T20:30:33Z)
• Contextual-Utterance Training for Automatic Speech Recognition [65.4571135368178]
  We propose a contextual-utterance training technique which makes use of the previous and future contextual utterances. Also, we propose a dual-mode contextual-utterance training technique for streaming automatic speech recognition (ASR) systems. The proposed technique is able to reduce both the WER and the average last token emission latency by more than 6% and 40ms relative.
  arXiv  Detail & Related papers  (2022-10-27T08:10:44Z)
• Can Visual Context Improve Automatic Speech Recognition for an Embodied Agent? [3.7311680121118345]
  We propose a new decoder biasing technique to incorporate the visual context while ensuring the ASR output does not degrade for incorrect context. We achieve a 59% relative reduction in WER from an unmodified ASR system.
  arXiv  Detail & Related papers  (2022-10-21T11:16:05Z)
• Neural Approaches to Conversational Information Retrieval [94.77863916314979]
  A conversational information retrieval (CIR) system is an information retrieval (IR) system with a conversational interface. Recent progress in deep learning has brought tremendous improvements in natural language processing (NLP) and conversational AI. This book surveys recent advances in CIR, focusing on neural approaches that have been developed in the last few years.
  arXiv  Detail & Related papers  (2022-01-13T19:04:59Z)
• Automatic Speech Recognition using limited vocabulary: A survey [0.0]
  An approach to design an ASR system targeting under-resourced languages is to start with a limited vocabulary. This paper aims to provide a comprehensive view of mechanisms behind ASR systems as well as techniques, tools, projects, recent contributions, and possibly future directions in ASR using a limited vocabulary.
  arXiv  Detail & Related papers  (2021-08-23T15:51:41Z)
• Quantifying Bias in Automatic Speech Recognition [28.301997555189462]
  This paper quantifies the bias of a Dutch SotA ASR system against gender, age, regional accents and non-native accents. Based on our findings, we suggest bias mitigation strategies for ASR development.
  arXiv  Detail & Related papers  (2021-03-28T12:52:03Z)
• LRSpeech: Extremely Low-Resource Speech Synthesis and Recognition [148.43282526983637]
  We develop LRSpeech, a TTS and ASR system for languages with low data cost. We conduct experiments on an experimental language (English) and a truly low-resource language (Lithuanian) to verify the effectiveness of LRSpeech. We are currently deploying LRSpeech into a commercialized cloud speech service to support TTS on more rare languages.
  arXiv  Detail & Related papers  (2020-08-09T08:16:33Z)
• Improving Readability for Automatic Speech Recognition Transcription [50.86019112545596]
  We propose a novel NLP task called ASR post-processing for readability (APR) APR aims to transform the noisy ASR output into a readable text for humans and downstream tasks while maintaining the semantic meaning of the speaker. We compare fine-tuned models based on several open-sourced and adapted pre-trained models with the traditional pipeline method.
  arXiv  Detail & Related papers  (2020-04-09T09:26:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.