发现传统和神经音频编码器存在性别与语言偏见
On the Language and Gender Biases in PSTN, VoIP and Neural Audio Codecs
- 分析超200万语音文件，对比PSTN、VoIP与神经编码器
- PSTN编码器显著偏向男性语音质量，神经编码器引入语言偏差
- 揭示语音技术隐性偏见，适合语音公平性研究者参考
近年来，语音技术中的公平性与包容性受到越来越多关注，尤其在自动语音识别和情感分析领域。当音频在处理前经过转码（如流媒体或实时应用），编码机制中的固有偏见可能导致差异。这不仅影响用户体验，还可能通过强化刻板印象加剧社会排斥。因此，音频编码机制的无偏至关重要。本文填补了语言与性别偏见在音频编码器方面研究稀缺的空白。通过对超过200万个多语言音频文件在代表性编码器（PSTN、VoIP与神经编码器）转码后的语音质量进行分析，结果表明：PSTN编码器在性别上存在明显偏差，而神经编码器则引入了语言层面的偏见。
原文摘要 · Abstract (English)
In recent years, there has been a growing focus on fairness and inclusivity within speech technology, particularly in areas such as automatic speech recognition and speech sentiment analysis. When audio is transcoded prior to processing, as is the case in streaming or real-time applications, any inherent bias in the coding mechanism may result in disparities. This not only affects user experience but can also have broader societal implications by perpetuating stereotypes and exclusion. Thus, it is important that audio coding mechanisms are unbiased. In this work, we contribute towards the scarce research with respect to language and gender biases of audio codecs. By analyzing the speech quality of over 2 million multilingual audio files after transcoding through a representative subset of codecs (PSTN, VoIP and neural), our results indicate that PSTN codecs are strongly biased in terms of gender and that neural codecs introduce language biases.
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