14 research outputs found
Current trends in multilingual speech processing
In this paper, we describe recent work at Idiap Research Institute in the domain of multilingual speech processing and provide some insights into emerging challenges for the research community. Multilingual speech processing has been a topic of ongoing interest to the research community for many years and the field is now receiving renewed interest owing to two strong driving forces. Firstly, technical advances in speech recognition and synthesis are posing new challenges and opportunities to researchers. For example, discriminative features are seeing wide application by the speech recognition community, but additional issues arise when using such features in a multilingual setting. Another example is the apparent convergence of speech recognition and speech synthesis technologies in the form of statistical parametric methodologies. This convergence enables the investigation of new approaches to unified modelling for automatic speech recognition and text-to-speech synthesis (TTS) as well as cross-lingual speaker adaptation for TTS. The second driving force is the impetus being provided by both government and industry for technologies to help break down domestic and international language barriers, these also being barriers to the expansion of policy and commerce. Speech-to-speech and speech-to-text translation are thus emerging as key technologies at the heart of which lies multilingual speech processin
Hierarchical Neural Network Architecture In Keyword Spotting
Keyword Spotting (KWS) provides the start signal of ASR problem, and thus it
is essential to ensure a high recall rate. However, its real-time property
requires low computation complexity. This contradiction inspires people to find
a suitable model which is small enough to perform well in multi environments.
To deal with this contradiction, we implement the Hierarchical Neural
Network(HNN), which is proved to be effective in many speech recognition
problems. HNN outperforms traditional DNN and CNN even though its model size
and computation complexity are slightly less. Also, its simple topology
structure makes easy to deploy on any device.Comment: To be submitted in part to IEEE ICASSP 201
Hierarchical Tandem Features for ASR in Mandarin
We apply multilayer perceptron (MLP) based hierarchical Tandem features to large vocabulary continuous speech recognition in Mandarin. Hierarchical Tandem features are estimated using a cascade of two MLP classifiers which are trained independently. The first classifier is trained on perceptual linear predictive coefficients with a 90 ms temporal context. The second classifier is trained using the phonetic class conditional probabilities estimated by the first MLP, but with a relatively longer temporal context of about 150 ms. Experiments on the Mandarin DARPA GALE eval06 dataset show significant reduction (about 7.6% relative) in character error rates by using hierarchical Tandem features over conventional Tandem features
Auditory processing-based features for improving speech recognition in adverse acoustic conditions
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Adaptation of speech recognition systems to selected real-world deployment conditions
Tato habilitační práce se zabývá problematikou adaptace systémů
rozpoznávání řeči na vybrané reálné podmínky nasazení. Je koncipována
jako sborník celkem dvanácti článků, které se touto problematikou
zabývají. Jde o publikace, jejichž jsem hlavním autorem
nebo spoluatorem, a které vznikly v rámci několika navazujících
výzkumných projektů. Na řešení těchto projektů jsem se
podílel jak v roli člena výzkumného týmu, tak i v roli řešitele nebo
spoluřešitele.
Publikace zařazené do tohoto sborníku lze rozdělit podle tématu
do tří hlavních skupin. Jejich společným jmenovatelem je
snaha přizpůsobit daný rozpoznávací systém novým podmínkám či
konkrétnímu faktoru, který významným způsobem ovlivňuje jeho
funkci či přesnost.
První skupina článků se zabývá úlohou neřízené adaptace na
mluvčího, kdy systém přizpůsobuje svoje parametry specifickým
hlasovým charakteristikám dané mluvící osoby. Druhá část práce
se pak věnuje problematice identifikace neřečových událostí na vstupu
do systému a související úloze rozpoznávání řeči s hlukem
(a zejména hudbou) na pozadí. Konečně třetí část práce se zabývá
přístupy, které umožňují přepis audio signálu obsahujícího promluvy
ve více než v jednom jazyce. Jde o metody adaptace existujícího
rozpoznávacího systému na nový jazyk a metody identifikace
jazyka z audio signálu.
Obě zmíněné identifikační úlohy jsou přitom vyšetřovány zejména
v náročném a méně probádaném režimu zpracování po jednotlivých
rámcích vstupního signálu, který je jako jediný vhodný pro on-line
nasazení, např. pro streamovaná data.This habilitation thesis deals with adaptation of automatic speech
recognition (ASR) systems to selected real-world deployment conditions.
It is presented in the form of a collection of twelve articles
dealing with this task; I am the main author or a co-author of these
articles. They were published during my work on several consecutive
research projects. I have participated in the solution of them
as a member of the research team as well as the investigator or a
co-investigator.
These articles can be divided into three main groups according to
their topics. They have in common the effort to adapt a particular
ASR system to a specific factor or deployment condition that affects
its function or accuracy.
The first group of articles is focused on an unsupervised speaker
adaptation task, where the ASR system adapts its parameters to
the specific voice characteristics of one particular speaker. The second
part deals with a) methods allowing the system to identify
non-speech events on the input, and b) the related task of recognition
of speech with non-speech events, particularly music, in the
background. Finally, the third part is devoted to the methods
that allow the transcription of an audio signal containing multilingual
utterances. It includes a) approaches for adapting the existing
recognition system to a new language and b) methods for identification
of the language from the audio signal.
The two mentioned identification tasks are in particular investigated
under the demanding and less explored frame-wise scenario,
which is the only one suitable for processing of on-line data streams