Neural speaker embeddings encode the speaker's speech characteristics through
a DNN model and are prevalent for speaker verification tasks. However, few
studies have investigated the usage of neural speaker embeddings for an ASR
system. In this work, we present our efforts w.r.t integrating neural speaker
embeddings into a conformer based hybrid HMM ASR system. For ASR, our improved
embedding extraction pipeline in combination with the Weighted-Simple-Add
integration method results in x-vector and c-vector reaching on par performance
with i-vectors. We further compare and analyze different speaker embeddings. We
present our acoustic model improvements obtained by switching from newbob
learning rate schedule to one cycle learning schedule resulting in a ~3%
relative WER reduction on Switchboard, additionally reducing the overall
training time by 17%. By further adding neural speaker embeddings, we gain
additional ~3% relative WER improvement on Hub5'00. Our best Conformer-based
hybrid ASR system with speaker embeddings achieves 9.0% WER on Hub5'00 and
Hub5'01 with training on SWB 300h.

本文研究了将神经说话者嵌入用于一个 ASR 系统，并通过基于 Conformer 的混合 HMM ASR 系统，在使用加权简单加法（Weighted-Simple-Add）集成方法时，展示了改进的嵌入提取流程，通过比较和分析不同的说话者嵌入来获得声学模型的改进，最终将最佳的 Conformer-based 混合 ASR 系统与说话者嵌入结合起来，获得了 9.0％的 WER 并在 Hub5'00 和 Hub5'01 上进行训练。

改进和分析用于 ASR 的神经说话人嵌入

Improving And Analyzing Neural Speaker Embeddings for ASR

The Voice Conversion Challenge 2020 is the third edition under its flagship
that promotes intra-lingual semiparallel and cross-lingual voice conversion
(VC). While the primary evaluation of the challenge submissions was done
through crowd-sourced listening tests, we also performed an objective
assessment of the submitted systems. The aim of the objective assessment is to
provide complementary performance analysis that may be more beneficial than the
time-consuming listening tests. In this study, we examined five types of
objective assessments using automatic speaker verification (ASV), neural
speaker embeddings, spoofing countermeasures, predicted mean opinion scores
(MOS), and automatic speech recognition (ASR). Each of these objective measures
assesses the VC output along different aspects. We observed that the
correlations of these objective assessments with the subjective results were
high for ASV, neural speaker embedding, and ASR, which makes them more
influential for predicting subjective test results. In addition, we performed
spoofing assessments on the submitted systems and identified some of the VC
methods showing a potentially high security risk.

该研究分析了五种客观评估方法对于語音转换（VC）的有效性，并发现与具有客观评估相比，排行榜依赖用户测试的次要评估可能效果较差；同时，还揭示了某些 VC 方法存在潜在的高安全风险。

2020 年语音转换挑战赛主观评级和欺骗性评估的预测

Predictions of Subjective Ratings and Spoofing Assessments of Voice  Conversion Challenge 2020 Submissions

While speaker adaptation for end-to-end speech synthesis using speaker
embeddings can produce good speaker similarity for speakers seen during
training, there remains a gap for zero-shot adaptation to unseen speakers. We
investigate multi-speaker modeling for end-to-end text-to-speech synthesis and
study the effects of different types of state-of-the-art neural speaker
embeddings on speaker similarity for unseen speakers. Learnable dictionary
encoding-based speaker embeddings with angular softmax loss can improve equal
error rates over x-vectors in a speaker verification task; these embeddings
also improve speaker similarity and naturalness for unseen speakers when used
for zero-shot adaptation to new speakers in end-to-end speech synthesis.

研究了使用多说话人建模中的神经发音人嵌入对零样本适应的影响，发现使用可学习字典编码的说话人嵌入，能够在说话人验证任务中提高等误差率，在未知说话人使用时提高零样本适应性，并提高端到端语音合成的说话人相似性和自然度。