2,369 research outputs found
Speaker segmentation and clustering
This survey focuses on two challenging speech processing topics, namely: speaker segmentation and speaker clustering. Speaker segmentation aims at finding speaker change points in an audio stream, whereas speaker clustering aims at grouping speech segments based on speaker characteristics. Model-based, metric-based, and hybrid speaker segmentation algorithms are reviewed. Concerning speaker clustering, deterministic and probabilistic algorithms are examined. A comparative assessment of the reviewed algorithms is undertaken, the algorithm advantages and disadvantages are indicated, insight to the algorithms is offered, and deductions as well as recommendations are given. Rich transcription and movie analysis are candidate applications that benefit from combined speaker segmentation and clustering. © 2007 Elsevier B.V. All rights reserved
Block-Online Multi-Channel Speech Enhancement Using DNN-Supported Relative Transfer Function Estimates
This work addresses the problem of block-online processing for multi-channel
speech enhancement. Such processing is vital in scenarios with moving speakers
and/or when very short utterances are processed, e.g., in voice assistant
scenarios. We consider several variants of a system that performs beamforming
supported by DNN-based voice activity detection (VAD) followed by
post-filtering. The speaker is targeted through estimating relative transfer
functions between microphones. Each block of the input signals is processed
independently in order to make the method applicable in highly dynamic
environments. Owing to the short length of the processed block, the statistics
required by the beamformer are estimated less precisely. The influence of this
inaccuracy is studied and compared to the processing regime when recordings are
treated as one block (batch processing). The experimental evaluation of the
proposed method is performed on large datasets of CHiME-4 and on another
dataset featuring moving target speaker. The experiments are evaluated in terms
of objective and perceptual criteria (such as signal-to-interference ratio
(SIR) or perceptual evaluation of speech quality (PESQ), respectively).
Moreover, word error rate (WER) achieved by a baseline automatic speech
recognition system is evaluated, for which the enhancement method serves as a
front-end solution. The results indicate that the proposed method is robust
with respect to short length of the processed block. Significant improvements
in terms of the criteria and WER are observed even for the block length of 250
ms.Comment: 10 pages, 8 figures, 4 tables. Modified version of the article
accepted for publication in IET Signal Processing journal. Original results
unchanged, additional experiments presented, refined discussion and
conclusion
Dialogue Act Modeling for Automatic Tagging and Recognition of Conversational Speech
We describe a statistical approach for modeling dialogue acts in
conversational speech, i.e., speech-act-like units such as Statement, Question,
Backchannel, Agreement, Disagreement, and Apology. Our model detects and
predicts dialogue acts based on lexical, collocational, and prosodic cues, as
well as on the discourse coherence of the dialogue act sequence. The dialogue
model is based on treating the discourse structure of a conversation as a
hidden Markov model and the individual dialogue acts as observations emanating
from the model states. Constraints on the likely sequence of dialogue acts are
modeled via a dialogue act n-gram. The statistical dialogue grammar is combined
with word n-grams, decision trees, and neural networks modeling the
idiosyncratic lexical and prosodic manifestations of each dialogue act. We
develop a probabilistic integration of speech recognition with dialogue
modeling, to improve both speech recognition and dialogue act classification
accuracy. Models are trained and evaluated using a large hand-labeled database
of 1,155 conversations from the Switchboard corpus of spontaneous
human-to-human telephone speech. We achieved good dialogue act labeling
accuracy (65% based on errorful, automatically recognized words and prosody,
and 71% based on word transcripts, compared to a chance baseline accuracy of
35% and human accuracy of 84%) and a small reduction in word recognition error.Comment: 35 pages, 5 figures. Changes in copy editing (note title spelling
changed
Methods for speaking style conversion from normal speech to high vocal effort speech
This thesis deals with vocal-effort-focused speaking style conversion (SSC). Specifically, we studied two topics on conversion of normal speech to high vocal effort. The first topic involves the conversion of normal speech to shouted speech. We employed this conversion in a speaker recognition system with vocal effort mismatch between test and enrollment utterances (shouted speech vs. normal speech). The mismatch causes a degradation of the system's speaker identification performance. As solution, we proposed a SSC system that included a novel spectral mapping, used along a statistical mapping technique, to transform the mel-frequency spectral energies of normal speech enrollment utterances towards their counterparts in shouted speech. We evaluated the proposed solution by comparing speaker identification rates for a state-of-the-art i-vector-based speaker recognition system, with and without applying SSC to the enrollment utterances. Our results showed that applying the proposed SSC pre-processing to the enrollment data improves considerably the speaker identification rates.
The second topic involves a normal-to-Lombard speech conversion. We proposed a vocoder-based parametric SSC system to perform the conversion. This system first extracts speech features using the vocoder. Next, a mapping technique, robust to data scarcity, maps the features. Finally, the vocoder synthesizes the mapped features into speech. We used two vocoders in the conversion system, for comparison: a glottal vocoder and the widely used STRAIGHT. We assessed the converted speech from the two vocoder cases with two subjective listening tests that measured similarity to Lombard speech and naturalness. The similarity subjective test showed that, for both vocoder cases, our proposed SSC system was able to convert normal speech to Lombard speech. The naturalness subjective test showed that the converted samples using the glottal vocoder were clearly more natural than those obtained with STRAIGHT
VOICE BIOMETRICS UNDER MISMATCHED NOISE CONDITIONS
This thesis describes research into effective voice biometrics (speaker recognition) under mismatched noise conditions. Over the last two decades, this class of biometrics has been the subject of considerable research due to its various applications in such areas as telephone banking, remote access control and surveillance. One of the main challenges associated with the deployment of voice biometrics in practice is that of undesired variations in speech characteristics caused by environmental noise. Such variations can in turn lead to a mismatch between the corresponding test and reference material from the same speaker. This is found to adversely affect the performance of speaker recognition in terms of accuracy.
To address the above problem, a novel approach is introduced and investigated. The proposed method is based on minimising the noise mismatch between reference speaker models and the given test utterance, and involves a new form of Test-Normalisation (T-Norm) for further enhancing matching scores under the aforementioned adverse operating conditions. Through experimental investigations, based on the two main classes of speaker recognition (i.e. verification/ open-set identification), it is shown that the proposed approach can significantly improve the performance accuracy under mismatched noise conditions.
In order to further improve the recognition accuracy in severe mismatch conditions, an approach to enhancing the above stated method is proposed. This, which involves providing a closer adjustment of the reference speaker models to the noise condition in the test utterance, is shown to considerably increase the accuracy in extreme cases of noisy test data. Moreover, to tackle the computational burden associated with the use of the enhanced approach with open-set identification, an efficient algorithm for its realisation in this context is introduced and evaluated.
The thesis presents a detailed description of the research undertaken, describes the experimental investigations and provides a thorough analysis of the outcomes
Acoustic Approaches to Gender and Accent Identification
There has been considerable research on the problems of speaker and language recognition
from samples of speech. A less researched problem is that of accent recognition. Although this
is a similar problem to language identification, di�erent accents of a language exhibit more
fine-grained di�erences between classes than languages. This presents a tougher problem
for traditional classification techniques. In this thesis, we propose and evaluate a number of
techniques for gender and accent classification. These techniques are novel modifications and
extensions to state of the art algorithms, and they result in enhanced performance on gender
and accent recognition.
The first part of the thesis focuses on the problem of gender identification, and presents a
technique that gives improved performance in situations where training and test conditions are
mismatched.
The bulk of this thesis is concerned with the application of the i-Vector technique to accent
identification, which is the most successful approach to acoustic classification to have emerged
in recent years. We show that it is possible to achieve high accuracy accent identification without
reliance on transcriptions and without utilising phoneme recognition algorithms. The thesis
describes various stages in the development of i-Vector based accent classification that improve
the standard approaches usually applied for speaker or language identification, which are
insu�cient. We demonstrate that very good accent identification performance is possible with
acoustic methods by considering di�erent i-Vector projections, frontend parameters, i-Vector
configuration parameters, and an optimised fusion of the resulting i-Vector classifiers we can
obtain from the same data.
We claim to have achieved the best accent identification performance on the test corpus
for acoustic methods, with up to 90% identification rate. This performance is even better than
previously reported acoustic-phonotactic based systems on the same corpus, and is very close
to performance obtained via transcription based accent identification. Finally, we demonstrate
that the utilization of our techniques for speech recognition purposes leads to considerably
lower word error rates.
Keywords: Accent Identification, Gender Identification, Speaker Identification, Gaussian
Mixture Model, Support Vector Machine, i-Vector, Factor Analysis, Feature Extraction, British
English, Prosody, Speech Recognition
Recommended from our members
I-vector estimation using informative priors for adaptation of deep neural networks
This is the author accepted manuscript. The final version is available from ISCA via http://www.isca-speech.org/archive/interspeech_2015/i15_2872.html
Supporting data for this paper is available at the http://www.repository.cam.ac.uk/handle/1810/248387 data repository.I-vectors are a well-known low-dimensional representation of speaker space and are becoming increasingly popular in adaptation of state-of-the-art deep neural network (DNN) acoustic models. One advantage of i-vectors is that they can be used with very little data, for example a single utterance. However, to improve robustness of the i-vector estimates with limited data, a prior is often used. Traditionally, a standard normal prior is applied to i-vectors, which is nevertheless not well suited to the increased variability of short utterances. This paper proposes a more informative prior, derived from the training data. As well as aiming to reduce the non-Gaussian behaviour of the i-vector space, it allows prior information at different levels, for example gender, to be used. Experiments on a US English Broadcast News (BN) transcription task for speaker and utterance i-vector adaptation show that more informative priors reduce the sensitivity to the quantity of data used to estimate the i-vector. The best configuration for this task was utterance-level test i-vectors enhanced with informative priors which gave a 13% relative reduction in word error rate over the baseline (no i-vectors) and a 5% over utterance-level test i-vectors with standard prior.This work was supported by EPSRC Programme Grant EP/I031022/1 (Natural Speech Technology)
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