用于提升自动说话人验证性能的语音丰富度
Phonetic Richness for Improved Automatic Speaker Verification
AI总结：
该研究提出基于语音直方图的语音丰富度度量指标，结合净语音时长校准说话人验证得分，在Voxceleb1基准上实现5.8%的EER相对提升，对短话语效果更优。
摘要：
在说话人验证系统的身份认证场景中，不同话语的认证价值并不相同。为应对多变的声学条件，评估测试话语的质量至关重要。本文研究发现，除话语的净语音时长外，语音丰富度也是话语质量的关键指标，对准确的说话人验证具有重要作用。研究利用自动说话人识别系统生成的转写文本，探索了多种基于语音直方图的语音丰富度计算方式。实验显示，所提出的语音丰富度度量指标在多个评估基准上与声纹认证得分呈正相关。此外，该度量指标与净语音时长结合可用于校准说话人验证得分，在Voxceleb1评估协议上取得了5.8%的等错误率（EER）相对提升。这种基于语音丰富度的校准方法对存在重复词汇的短话语增益更显著。
英文摘要：
When it comes to authentication in speaker verification systems, not all utterances are created equal. It is essential to estimate the quality of test utterances in order to account for varying acoustic conditions. In addition to the net-speech duration of an utterance, it is observed in this paper that phonetic richness is also a key indicator of utterance quality, playing a significant role in accurate speaker verification. Several phonetic histogram based formulations of phonetic richness are explored using transcripts obtained from an automatic speaker recognition system. The proposed phonetic richness measure is found to be positively correlated with voice authentication scores across evaluation benchmarks. Additionally, the proposed measure in combination with net speech helps in calibrating the speaker verification scores, obtaining a relative EER improvement of 5.8% on the Voxceleb1 evaluation protocol. The proposed phonetic richness based calibration provides higher benefit for short utterances with repeated words.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
