End-to-End Open Vocabulary Keyword Search With Multilingual Neural Representations

Conventional keyword search systems operate on automatic speech recognition (ASR) outputs, which causes them to have a complex indexing and search pipeline. This has led to interest in ASR-free approaches to simplify the search procedure. We recently proposed a neural ASR-free keyword search model which achieves competitive performance while maintaining an efficient and simplified pipeline, where queries and documents are encoded with a pair of recurrent neural network encoders and the encodings are combined with a dot-product. In this article, we extend this work with multilingual pretraining and detailed analysis of the model. Our experiments show that the proposed multilingual training significantly improves the model performance and that despite not matching a strong ASR-based conventional keyword search system for short queries and queries comprising in-vocabulary words, the proposed model outperforms the ASR-based system for long queries and queries that do not appear in the training data.Comment: Accepted by IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP), 202

Pronunciation Modeling By Sharing Gaussian Densities Across Phonetic Models

Conversational speech exhibits considerable pronunciation variability, which has been shown to have a detrimental effect on the accuracy of automatic speech recognition. There have been many attempts to model pronunciation variation, including the use of decision-trees to generate alternate word pronunciations from phonemic baseforms. Use of such pronunciation models during recognition is known to improve accuracy. This paper describes the use of such pronunciation models during acoustic model training. Subtle difficulties in the straightforward use of alternatives to canonical pronunciations are first illustrated: it is shown that simply improving the accuracy of the phonetic transcription used for acoustic model training is of little benefit. Analysis of this paradox leads to a new method of accommodating nonstandard pronunciations: rather than allowing a phoneme in the canonical pronunciation to be realized as one of a few distinct alternate phones predicted by the pronunciation model, the HMM states of the phoneme's model are instead allowed to share Gaussian mixture components with the HMM states of the model of the alternate realization. Qualitatively, this amounts to making a soft decision about which surface-form is realized. Quantitative experiments on the Switchboard corpus show that this method improves accuracy by 1.7% (absolute)

Discriminative syntactic language modeling for speech recognition

We describe a method for discriminative training of a language model that makes use of syntactic features. We follow a reranking approach, where a baseline recogniser is used to produce 1000-best output for each acoustic input, and a second “reranking ” model is then used to choose an utterance from these 1000-best lists. The reranking model makes use of syntactic features together with a parameter estimation method that is based on the perceptron algorithm. We describe experiments on the Switchboard speech recognition task. The syntactic features provide an additional 0.3 % reduction in test–set error rate beyond the model of (Roark et al., 2004a; Roark et al., 2004b) (significant at p < 0.001), which makes use of a discriminatively trained n-gram model, giving a total reduction of 1.2 % over the baseline Switchboard system.