When a speaker verification (SV) system operates far from the sound sourced,
significant challenges arise due to the interference of noise and
reverberation. Studies have shown that incorporating phonetic information into
speaker embedding can improve the performance of text-independent SV. Inspired
by this observation, we propose a joint-training speech recognition and speaker
recognition (JTSS) framework to exploit phonetic content for far-field SV. The
framework encourages speaker embeddings to preserve phonetic information by
matching the frame-based feature maps of a speaker embedding network with
wav2vec's vectors. The intuition is that phonetic information can preserve
low-level acoustic dynamics with speaker information and thus partly compensate
for the degradation due to noise and reverberation. Results show that the
proposed framework outperforms the standard speaker embedding on the VOiCES
Challenge 2019 evaluation set and the VoxCeleb1 test set. This indicates that
leveraging phonetic information under far-field conditions is effective for
learning robust speaker representations.

提出了一种联合训练语音识别和说话人识别 (JTSS) 框架，以利用语音中的音素内容来进行远场说话人验证，结果表明在远场环境下利用音素信息是学习强健说话人表示的有效方法。