On the Language and Gender Biases in PSTN, VoIP and Neural Audio Codecs
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| Format: | Preprint |
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2025
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| _version_ | 1866918147740663808 |
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| author | Altwlkany, Kemal Kuric, Amar Lacic, Emanuel |
| author_facet | Altwlkany, Kemal Kuric, Amar Lacic, Emanuel |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2506_02545 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | On the Language and Gender Biases in PSTN, VoIP and Neural Audio Codecs Altwlkany, Kemal Kuric, Amar Lacic, Emanuel Sound Audio and Speech Processing 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. |
| title | On the Language and Gender Biases in PSTN, VoIP and Neural Audio Codecs |
| topic | Sound Audio and Speech Processing |
| url | https://arxiv.org/abs/2506.02545 |