We consider the problem of training speech recognition systems without using
any labeled data, under the assumption that the learner can only access to the
input utterances and a phoneme language model estimated from a non-overlapping
corpus. We propose a fully unsupervised learning algorithm that alternates
between solving two sub-problems: (i) learn a phoneme classifier for a given
set of phoneme segmentation boundaries, and (ii) refining the phoneme
boundaries based on a given classifier. To solve the first sub-problem, we
introduce a novel unsupervised cost function named Segmental Empirical Output
Distribution Matching, which generalizes the work in (Liu et al., 2017) to
segmental structures. For the second sub-problem, we develop an approximate MAP
approach to refining the boundaries obtained from Wang et al. (2017).
Experimental results on TIMIT dataset demonstrate the success of this fully
unsupervised phoneme recognition system, which achieves a phone error rate
(PER) of 41.6%. Although it is still far away from the state-of-the-art
supervised systems, we show that with oracle boundaries and matching language
model, the PER could be improved to 32.5%.This performance approaches the
supervised system of the same model architecture, demonstrating the great
potential of the proposed method.

本研究提出了一种完全无监督的学习算法，可在不使用标记数据的情况下训练语音识别系统，其关键在于使用音位语言模型和输入话语。该算法在解决两个子问题 (即学习给定音位划分边界下的音位分类器和更新音位边界) 上交替进行，并在 TIMIT 数据集上获得了令人满意的性能提升。

基于分段经验输出分布匹配的无监督语音识别

Unsupervised Speech Recognition via Segmental Empirical Output  Distribution Matching

In this paper, we propose a Convolutional Neural Network (CNN) based speaker
recognition model for extracting robust speaker embeddings. The embedding can
be extracted efficiently with linear activation in the embedding layer. To
understand how the speaker recognition model operates with text-independent
input, we modify the structure to extract frame-level speaker embeddings from
each hidden layer. We feed utterances from the TIMIT dataset to the trained
network and use several proxy tasks to study the networks ability to represent
speech input and differentiate voice identity. We found that the networks are
better at discriminating broad phonetic classes than individual phonemes. In
particular, frame-level embeddings that belong to the same phonetic classes are
similar (based on cosine distance) for the same speaker. The frame level
representation also allows us to analyze the networks at the frame level, and
has the potential for other analyses to improve speaker recognition.

本文提出了一种基于卷积神经网络（CNN）的说话人识别模型，可以提取鲁棒的说话者嵌入，并通过 TIMIT 数据集对其进行了训练和多个代理任务来研究网络在区分语音输入和声音身份方面的能力，其中发现该网络更擅长区分相似的语音类别而不是单个音素，这一帧级别的表示法还允许我们在帧级别上分析网络，并具有提高说话人识别的其他分析的潜力。

面向文本独立说话人识别的帧级别说话人特征向量及端到端模型分析

Frame-level speaker embeddings for text-independent speaker recognition  and analysis of end-to-end model

We replace the Hidden Markov Model (HMM) which is traditionally used in in
continuous speech recognition with a bi-directional recurrent neural network
encoder coupled to a recurrent neural network decoder that directly emits a
stream of phonemes. The alignment between the input and output sequences is
established using an attention mechanism: the decoder emits each symbol based
on a context created with a subset of input symbols elected by the attention
mechanism. We report initial results demonstrating that this new approach
achieves phoneme error rates that are comparable to the state-of-the-art
HMM-based decoders, on the TIMIT dataset.

本文提出了基于双向循环神经网络编码器和递归神经网络解码器的语音识别方法，使用关注机制对输入与输出序列对齐以较高准确性地识别音素，且在 TIMIT 数据集上与传统的 HMM 方法相当。