1,018 research outputs found
Porting concepts from DNNs back to GMMs
Deep neural networks (DNNs) have been shown to outperform Gaussian Mixture Models (GMM) on a variety of speech recognition benchmarks. In this paper we analyze the differences between the DNN and GMM modeling techniques and port the best ideas from the DNN-based modeling to a GMM-based system. By going both deep (multiple layers) and wide (multiple parallel sub-models) and by sharing model parameters, we are able to close the gap between the two modeling techniques on the TIMIT database. Since the 'deep' GMMs retain the maximum-likelihood trained Gaussians as first layer, advanced techniques such as speaker adaptation and model-based noise robustness can be readily incorporated. Regardless of their similarities, the DNNs and the deep GMMs still show a sufficient amount of complementarity to allow effective system combination
Emotion Recognition from Acted and Spontaneous Speech
Dizertační práce se zabývá rozpoznáním emočního stavu mluvčích z řečového signálu. Práce je rozdělena do dvou hlavních častí, první část popisuju navržené metody pro rozpoznání emočního stavu z hraných databází. V rámci této části jsou představeny výsledky rozpoznání použitím dvou různých databází s různými jazyky. Hlavními přínosy této části je detailní analýza rozsáhlé škály různých příznaků získaných z řečového signálu, návrh nových klasifikačních architektur jako je například „emoční párování“ a návrh nové metody pro mapování diskrétních emočních stavů do dvou dimenzionálního prostoru. Druhá část se zabývá rozpoznáním emočních stavů z databáze spontánní řeči, která byla získána ze záznamů hovorů z reálných call center. Poznatky z analýzy a návrhu metod rozpoznání z hrané řeči byly využity pro návrh nového systému pro rozpoznání sedmi spontánních emočních stavů. Jádrem navrženého přístupu je komplexní klasifikační architektura založena na fúzi různých systémů. Práce se dále zabývá vlivem emočního stavu mluvčího na úspěšnosti rozpoznání pohlaví a návrhem systému pro automatickou detekci úspěšných hovorů v call centrech na základě analýzy parametrů dialogu mezi účastníky telefonních hovorů.Doctoral thesis deals with emotion recognition from speech signals. The thesis is divided into two main parts; the first part describes proposed approaches for emotion recognition using two different multilingual databases of acted emotional speech. The main contributions of this part are detailed analysis of a big set of acoustic features, new classification schemes for vocal emotion recognition such as “emotion coupling” and new method for mapping discrete emotions into two-dimensional space. The second part of this thesis is devoted to emotion recognition using multilingual databases of spontaneous emotional speech, which is based on telephone records obtained from real call centers. The knowledge gained from experiments with emotion recognition from acted speech was exploited to design a new approach for classifying seven emotional states. The core of the proposed approach is a complex classification architecture based on the fusion of different systems. The thesis also examines the influence of speaker’s emotional state on gender recognition performance and proposes system for automatic identification of successful phone calls in call center by means of dialogue features.
A Speaker Diarization System for Studying Peer-Led Team Learning Groups
Peer-led team learning (PLTL) is a model for teaching STEM courses where
small student groups meet periodically to collaboratively discuss coursework.
Automatic analysis of PLTL sessions would help education researchers to get
insight into how learning outcomes are impacted by individual participation,
group behavior, team dynamics, etc.. Towards this, speech and language
technology can help, and speaker diarization technology will lay the foundation
for analysis. In this study, a new corpus is established called CRSS-PLTL, that
contains speech data from 5 PLTL teams over a semester (10 sessions per team
with 5-to-8 participants in each team). In CRSS-PLTL, every participant wears a
LENA device (portable audio recorder) that provides multiple audio recordings
of the event. Our proposed solution is unsupervised and contains a new online
speaker change detection algorithm, termed G 3 algorithm in conjunction with
Hausdorff-distance based clustering to provide improved detection accuracy.
Additionally, we also exploit cross channel information to refine our
diarization hypothesis. The proposed system provides good improvements in
diarization error rate (DER) over the baseline LIUM system. We also present
higher level analysis such as the number of conversational turns taken in a
session, and speaking-time duration (participation) for each speaker.Comment: 5 Pages, 2 Figures, 2 Tables, Proceedings of INTERSPEECH 2016, San
Francisco, US
Very Deep Convolutional Neural Networks for Robust Speech Recognition
This paper describes the extension and optimization of our previous work on
very deep convolutional neural networks (CNNs) for effective recognition of
noisy speech in the Aurora 4 task. The appropriate number of convolutional
layers, the sizes of the filters, pooling operations and input feature maps are
all modified: the filter and pooling sizes are reduced and dimensions of input
feature maps are extended to allow adding more convolutional layers.
Furthermore appropriate input padding and input feature map selection
strategies are developed. In addition, an adaptation framework using joint
training of very deep CNN with auxiliary features i-vector and fMLLR features
is developed. These modifications give substantial word error rate reductions
over the standard CNN used as baseline. Finally the very deep CNN is combined
with an LSTM-RNN acoustic model and it is shown that state-level weighted log
likelihood score combination in a joint acoustic model decoding scheme is very
effective. On the Aurora 4 task, the very deep CNN achieves a WER of 8.81%,
further 7.99% with auxiliary feature joint training, and 7.09% with LSTM-RNN
joint decoding.Comment: accepted by SLT 201
Analysis of Large-Scale SVM Training Algorithms for Language and Speaker Recognition
This paper compares a set of large scale support vector machine (SVM) training algorithms for language and speaker recognition tasks.We analyze five approaches for training phonetic and acoustic SVM models for language recognition. We compare the performance of these approaches as a function of the training time required by each of them to reach convergence, and we discuss their scalability towards large corpora. Two of these algorithms can be used in speaker recognition to train a SVM that classifies pairs of utterances as either belonging to the same speaker or to two different speakers. Our results show that the accuracy of these algorithms is asymptotically equivalent, but they have different behavior with respect to the time required to converge. Some of these algorithms not only scale linearly with the training set size, but are also able to give their best results after just a few iterations. State-of-the-art performance has been obtained in the female subset of the NIST 2010 Speaker Recognition Evaluation extended core test using a single SVM syste
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