1,086 research outputs found
Multi-Dialect Speech Recognition With A Single Sequence-To-Sequence Model
Sequence-to-sequence models provide a simple and elegant solution for
building speech recognition systems by folding separate components of a typical
system, namely acoustic (AM), pronunciation (PM) and language (LM) models into
a single neural network. In this work, we look at one such sequence-to-sequence
model, namely listen, attend and spell (LAS), and explore the possibility of
training a single model to serve different English dialects, which simplifies
the process of training multi-dialect systems without the need for separate AM,
PM and LMs for each dialect. We show that simply pooling the data from all
dialects into one LAS model falls behind the performance of a model fine-tuned
on each dialect. We then look at incorporating dialect-specific information
into the model, both by modifying the training targets by inserting the dialect
symbol at the end of the original grapheme sequence and also feeding a 1-hot
representation of the dialect information into all layers of the model.
Experimental results on seven English dialects show that our proposed system is
effective in modeling dialect variations within a single LAS model,
outperforming a LAS model trained individually on each of the seven dialects by
3.1 ~ 16.5% relative.Comment: submitted to ICASSP 201
Joint morphological-lexical language modeling for processing morphologically rich languages with application to dialectal Arabic
Language modeling for an inflected language
such as Arabic poses new challenges for speech recognition and
machine translation due to its rich morphology. Rich morphology
results in large increases in out-of-vocabulary (OOV) rate and
poor language model parameter estimation in the absence of large
quantities of data. In this study, we present a joint
morphological-lexical language model (JMLLM) that takes
advantage of Arabic morphology. JMLLM combines
morphological segments with the underlying lexical items and
additional available information sources with regards to
morphological segments and lexical items in a single joint model.
Joint representation and modeling of morphological and lexical
items reduces the OOV rate and provides smooth probability
estimates while keeping the predictive power of whole words.
Speech recognition and machine translation experiments in
dialectal-Arabic show improvements over word and morpheme
based trigram language models. We also show that as the
tightness of integration between different information sources
increases, both speech recognition and machine translation
performances improve
The 2013 KIT Quaero Speech-to-Text System for French
This paper describes our Speech-to-Text (STT) system for French, which was developed as part of our efforts in the Quaero program for the 2013 evaluation. Our STT system consists of six subsystems which were created by combining multiple complementary sources of pronunciation modeling including graphemes with various feature front-ends based on deep neural networks and tonal features. Both speaker-independent and speaker adaptively trained versions of the systems were built. The resulting systems were then combined via confusion network combination and crossadaptation. Through progressive advances and system combination we reach a word error rate (WER) of 16.5% on the 2012 Quaero evaluation data
Reducing speech recognition time and memory use by means of compound (de-)composition
This paper tackles the problem of Out Of Vocabulary words in Automatic Speech Transcription applications for a compound language (Dutch). A seemingly attractive way to reduce the amount of OOV words in compound languages is to extend the AST system with a compound (de-)composition module. However, thus far, successful implementations of this approach are rather scarce.
We developed a novel data driven compound (de-)composition module and tested it in two different AST experiments. For equal lexicon sizes, we see that our compound processor lowers the OOV rate. Moreover we are able to transform that gain in OOV rate into a reduction of the Word Error Rate of the transcription system. Using our approach we built a system with an 84K lexicon that performs as accurately as a baseline system with a 168K lexicon, but our system is 5-6% faster and requires about 50% less storage for the lexical component, even though this component is encoded in an optimal way (prefix-suffix tree compression)
A spoken document retrieval application in the oral history domain
The application of automatic speech recognition in the broadcast news domain is well studied. Recognition performance is generally high and accordingly, spoken document retrieval can successfully be applied in this domain, as demonstrated by a number of commercial systems. In other domains, a similar recognition performance is hard to obtain, or even far out of reach, for example due to lack of suitable training material. This is a serious impediment for the successful application of spoken document retrieval techniques for other data then news. This paper outlines our first steps towards a retrieval system that can automatically be adapted to new domains. We discuss our experience with a recently implemented spoken document retrieval application attached to a web-portal that aims at the disclosure of a multimedia data collection in the oral history domain. The paper illustrates that simply deploying an off-theshelf\ud
broadcast news system in this task domain will produce error rates that are too high to be useful for retrieval tasks. By applying adaptation techniques on the acoustic level and language model level, system performance can be improved considerably, but additional research on unsupervised adaptation and search interfaces is required to create an adequate search environment based on speech transcripts
Advances in All-Neural Speech Recognition
This paper advances the design of CTC-based all-neural (or end-to-end) speech
recognizers. We propose a novel symbol inventory, and a novel iterated-CTC
method in which a second system is used to transform a noisy initial output
into a cleaner version. We present a number of stabilization and initialization
methods we have found useful in training these networks. We evaluate our system
on the commonly used NIST 2000 conversational telephony test set, and
significantly exceed the previously published performance of similar systems,
both with and without the use of an external language model and decoding
technology
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