Fix it where it fails: Pronunciation learning by mining error corrections from speech logs

The pronunciation dictionary, or lexicon, is an essential component in an automatic speech recognition (ASR) system in that incorrect pronunciations cause systematic misrecognitions. It typically con-sists of a list of word-pronunciation pairs written by linguists, and a grapheme-to-phoneme (G2P) engine to generate pronunciations for words not in the list. The hand-generated list can never keep pace with the growing vocabulary of a live speech recognition sys-tem, and the G2P is usually of limited accuracy. This is especially true for proper names whose pronunciations may be influenced by various historical or foreign-origin factors. In this paper, we pro-pose a language-independent approach to detect misrecognitions and their corrections from voice search logs. We learn previously un-known pronunciations from this data, and demonstrate that they sig-nificantly improve the quality of a production-quality speech recog-nition system. Index Terms — speech recognition, pronunciation learning, data extraction, logistic regression 1

Tacotron: Towards End-to-End Speech Synthesis

A text-to-speech synthesis system typically consists of multiple stages, such as a text analysis frontend, an acoustic model and an audio synthesis module. Building these components often requires extensive domain expertise and may contain brittle design choices. In this paper, we present Tacotron, an end-to-end generative text-to-speech model that synthesizes speech directly from characters. Given pairs, the model can be trained completely from scratch with random initialization. We present several key techniques to make the sequence-to-sequence framework perform well for this challenging task. Tacotron achieves a 3.82 subjective 5-scale mean opinion score on US English, outperforming a production parametric system in terms of naturalness. In addition, since Tacotron generates speech at the frame level, it's substantially faster than sample-level autoregressive methods.Comment: Submitted to Interspeech 2017. v2 changed paper title to be consistent with our conference submission (no content change other than typo fixes

A systematic comparison of phrase table pruning techniques.

Abstract When trained on very large parallel corpora, the phrase table component of a machine translation system grows to consume vast computational resources. In this paper, we introduce a novel pruning criterion that places phrase table pruning on a sound theoretical foundation. Systematic experiments on four language pairs under various data conditions show that our principled approach is superior to existing ad hoc pruning methods