75 research outputs found
Subphonetic Modeling for Speech Recognition
How to capture important acoustic clues and estimate essential parameters reliably is one of the central issues in speech recognition, since we will never have sufficient training data to model various acoustic-phonetic phenomena. Successful examples include subword models with many smoothing techniques. In comparison with subword models, subphonetic modeling may provide a finer level of details. We propose to model subphonetic events with Markov states and treat the state in phonetic hidden Markov models as our basic subphonetic unit-- senone. A word model is a concatenation of state-dependent senones and senones can be shared across different word models. Senones not only allow parameter sharing, but also enable pronunciation optimization and new word learning, where the phonetic baseform is replaced by the senonic baseform. In this paper, we report preliminary subphonetic modeling results, which not only significantly reduced the word error rate for speaker-independent continuous speech recognition but also demonstrated a novel application for new word learning.
The Microsoft 2017 Conversational Speech Recognition System
We describe the 2017 version of Microsoft's conversational speech recognition
system, in which we update our 2016 system with recent developments in
neural-network-based acoustic and language modeling to further advance the
state of the art on the Switchboard speech recognition task. The system adds a
CNN-BLSTM acoustic model to the set of model architectures we combined
previously, and includes character-based and dialog session aware LSTM language
models in rescoring. For system combination we adopt a two-stage approach,
whereby subsets of acoustic models are first combined at the senone/frame
level, followed by a word-level voting via confusion networks. We also added a
confusion network rescoring step after system combination. The resulting system
yields a 5.1\% word error rate on the 2000 Switchboard evaluation set
Transformer-based Acoustic Modeling for Hybrid Speech Recognition
We propose and evaluate transformer-based acoustic models (AMs) for hybrid
speech recognition. Several modeling choices are discussed in this work,
including various positional embedding methods and an iterated loss to enable
training deep transformers. We also present a preliminary study of using
limited right context in transformer models, which makes it possible for
streaming applications. We demonstrate that on the widely used Librispeech
benchmark, our transformer-based AM outperforms the best published hybrid
result by 19% to 26% relative when the standard n-gram language model (LM) is
used. Combined with neural network LM for rescoring, our proposed approach
achieves state-of-the-art results on Librispeech. Our findings are also
confirmed on a much larger internal dataset.Comment: to appear in ICASSP 202
Analyzing Autoencoder-Based Acoustic Word Embeddings
Recent studies have introduced methods for learning acoustic word embeddings
(AWEs)---fixed-size vector representations of words which encode their acoustic
features. Despite the widespread use of AWEs in speech processing research,
they have only been evaluated quantitatively in their ability to discriminate
between whole word tokens. To better understand the applications of AWEs in
various downstream tasks and in cognitive modeling, we need to analyze the
representation spaces of AWEs. Here we analyze basic properties of AWE spaces
learned by a sequence-to-sequence encoder-decoder model in six typologically
diverse languages. We first show that these AWEs preserve some information
about words' absolute duration and speaker. At the same time, the
representation space of these AWEs is organized such that the distance between
words' embeddings increases with those words' phonetic dissimilarity. Finally,
the AWEs exhibit a word onset bias, similar to patterns reported in various
studies on human speech processing and lexical access. We argue this is a
promising result and encourage further evaluation of AWEs as a potentially
useful tool in cognitive science, which could provide a link between speech
processing and lexical memory.Comment: 6 pages, 7 figures, accepted to BAICS workshop (ICLR2020
Automatsko raspoznavanje hrvatskoga govora velikoga vokabulara
This paper presents procedures used for development of a Croatian large vocabulary automatic speech recognition system (LVASR). The proposed acoustic model is based on context-dependent triphone hidden Markov models and Croatian phonetic rules. Different acoustic and language models, developed using a large collection of Croatian speech, are discussed and compared. The paper proposes the best feature vectors and acoustic modeling procedures using which lowest word error rates for Croatian speech are achieved. In addition, Croatian language modeling procedures are evaluated and adopted for speaker independent spontaneous speech recognition. Presented experiments and results show that the proposed approach for automatic speech recognition using context-dependent acoustic modeling based on Croatian phonetic rules and a parameter tying procedure can be used for efļ¬cient Croatian large vocabulary speech recognition with word error rates below 5%.Älanak prikazuje postupke akustiÄkog i jeziÄnog modeliranja sustava za automatsko raspoznavanje hrvatskoga govora velikoga vokabulara. Predloženi akustiÄki modeli su zasnovani na kontekstno-ovisnim skrivenim Markovljevim modelima trifona i hrvatskim fonetskim pravilima. Na hrvatskome govoru prikupljenom u korpusu su ocjenjeni i usporeÄeni razliÄiti akustiÄki i jeziÄni modeli. U Älanku su usporeÄ eni i predloženi postupci za izraÄun vektora znaÄajki za akustiÄko modeliranje kao i sam pristup akustiÄkome modeliranju hrvatskoga govora s kojim je postignuta najmanja mjera pogreÅ”no raspoznatih rijeÄi. Predstavljeni su rezultati raspoznavanja spontanog hrvatskog govora neovisni o govorniku. Postignuti rezultati eksperimenata s mjerom pogreÅ”ke ispod 5% ukazuju na primjerenost predloženih postupaka za automatsko raspoznavanje hrvatskoga govora velikoga vokabulara pomoÄu vezanih kontekstnoovisnih akustiÄkih modela na osnovu hrvatskih fonetskih pravila
- ā¦