We investigate unsupervised models that can map a variable-duration speech
segment to a fixed-dimensional representation. In settings where unlabelled
speech is the only available resource, such acoustic word embeddings can form
the basis for "zero-resource" speech search, discovery and indexing systems.
Most existing unsupervised embedding methods still use some supervision, such
as word or phoneme boundaries. Here we propose the encoder-decoder
correspondence autoencoder (EncDec-CAE), which, instead of true word segments,
uses automatically discovered segments: an unsupervised term discovery system
finds pairs of words of the same unknown type, and the EncDec-CAE is trained to
reconstruct one word given the other as input. We compare it to a standard
encoder-decoder autoencoder (AE), a variational AE with a prior over its latent
embedding, and downsampling. EncDec-CAE outperforms its closest competitor by
24% relative in average precision on two languages in a word discrimination
task.

本文提出了一种基于编码器 - 解码器一一对应自编码器 (EncDec-CAE) 的无监督学习方法，用于从可变长度的语音以及无标签的语音数据中提取出与固定维度表示形式相对应的词嵌入，从而在无资源语音搜索、发现和索引系统中发挥重要作用，并在词辨别任务中相对于最接近的竞争对手提高了 24% 的平均精度。

使用编码器 - 解码器模型中的弱自上而下约束实现真正的无监督声学词嵌入

Truly unsupervised acoustic word embeddings using weak top-down  constraints in encoder-decoder models

We explore the problem of translating speech to text in low-resource
scenarios where neither automatic speech recognition (ASR) nor machine
translation (MT) are available, but we have training data in the form of audio
paired with text translations. We present the first system for this problem
applied to a realistic multi-speaker dataset, the CALLHOME Spanish-English
speech translation corpus. Our approach uses unsupervised term discovery (UTD)
to cluster repeated patterns in the audio, creating a pseudotext, which we pair
with translations to create a parallel text and train a simple bag-of-words MT
model. We identify the challenges faced by the system, finding that the
difficulty of cross-speaker UTD results in low recall, but that our system is
still able to correctly translate some content words in test data.

本文探讨语音转文本翻译的问题，使用无监督术语发现技术将音频文件转化为伪文本，并使用其进行简单的词袋机翻译，面对跨讲话者无监督术语发现的难题，系统实现了低召回率的跨语音翻译。

无需语音识别的语音到文本翻译

Towards speech-to-text translation without speech recognition

Recent work on unsupervised term discovery (UTD) aims to identify and cluster
repeated word-like units from audio alone. These systems are promising for some
very low-resource languages where transcribed audio is unavailable, or where no
written form of the language exists. However, in some cases it may still be
feasible (e.g., through crowdsourcing) to obtain (possibly noisy) text
translations of the audio. If so, this information could be used as a source of
side information to improve UTD. Here, we present a simple method for rescoring
the output of a UTD system using text translations, and test it on a corpus of
Spanish audio with English translations. We show that it greatly improves the
average precision of the results over a wide range of system configurations and
data preprocessing methods.

本文介绍了一种使用文本翻译来改善无监督术语发现系统（UTD）结果的简单方法，并在一组西班牙语音频和英语翻译的语料库上进行了测试，结果表明，该方法在广泛的系统配置和数据预处理方法下大大提高了结果的平均精度。