18,446 research outputs found
Prosodic-Enhanced Siamese Convolutional Neural Networks for Cross-Device Text-Independent Speaker Verification
In this paper a novel cross-device text-independent speaker verification
architecture is proposed. Majority of the state-of-the-art deep architectures
that are used for speaker verification tasks consider Mel-frequency cepstral
coefficients. In contrast, our proposed Siamese convolutional neural network
architecture uses Mel-frequency spectrogram coefficients to benefit from the
dependency of the adjacent spectro-temporal features. Moreover, although
spectro-temporal features have proved to be highly reliable in speaker
verification models, they only represent some aspects of short-term acoustic
level traits of the speaker's voice. However, the human voice consists of
several linguistic levels such as acoustic, lexicon, prosody, and phonetics,
that can be utilized in speaker verification models. To compensate for these
inherited shortcomings in spectro-temporal features, we propose to enhance the
proposed Siamese convolutional neural network architecture by deploying a
multilayer perceptron network to incorporate the prosodic, jitter, and shimmer
features. The proposed end-to-end verification architecture performs feature
extraction and verification simultaneously. This proposed architecture displays
significant improvement over classical signal processing approaches and deep
algorithms for forensic cross-device speaker verification.Comment: Accepted in 9th IEEE International Conference on Biometrics: Theory,
Applications, and Systems (BTAS 2018
ACCDIST: A Metric for comparing speakers' accents
This paper introduces a new metric for the quantitative assessment of the similarity of speakers' accents. The ACCDIST metric is based on the correlation of inter-segment distance tables across speakers or groups. Basing the metric on segment similarity within a speaker ensures that it is sensitive to the speaker's pronunciation system rather than to his or her voice characteristics. The metric is shown to have an error rate of only 11% on the accent classification of speakers into 14 English regional accents of the British Isles, half the error rate of a metric based on spectral information directly. The metric may also be useful for cluster analysis of accent groups
End-to-end Audiovisual Speech Activity Detection with Bimodal Recurrent Neural Models
Speech activity detection (SAD) plays an important role in current speech
processing systems, including automatic speech recognition (ASR). SAD is
particularly difficult in environments with acoustic noise. A practical
solution is to incorporate visual information, increasing the robustness of the
SAD approach. An audiovisual system has the advantage of being robust to
different speech modes (e.g., whisper speech) or background noise. Recent
advances in audiovisual speech processing using deep learning have opened
opportunities to capture in a principled way the temporal relationships between
acoustic and visual features. This study explores this idea proposing a
\emph{bimodal recurrent neural network} (BRNN) framework for SAD. The approach
models the temporal dynamic of the sequential audiovisual data, improving the
accuracy and robustness of the proposed SAD system. Instead of estimating
hand-crafted features, the study investigates an end-to-end training approach,
where acoustic and visual features are directly learned from the raw data
during training. The experimental evaluation considers a large audiovisual
corpus with over 60.8 hours of recordings, collected from 105 speakers. The
results demonstrate that the proposed framework leads to absolute improvements
up to 1.2% under practical scenarios over a VAD baseline using only audio
implemented with deep neural network (DNN). The proposed approach achieves
92.7% F1-score when it is evaluated using the sensors from a portable tablet
under noisy acoustic environment, which is only 1.0% lower than the performance
obtained under ideal conditions (e.g., clean speech obtained with a high
definition camera and a close-talking microphone).Comment: Submitted to Speech Communicatio
Assessing the Prosody of Non-Native Speakers of English: Measures and Feature Sets
In this paper, we describe a new database with audio recordings of non-native (L2) speakers of English, and the perceptual evaluation experiment conducted with native English speakers for assessing the prosody of each recording. These annotations are then used to compute the gold standard using different methods, and a series of regression experiments is conducted to evaluate their impact on the performance of a regression model predicting the degree of Abstract naturalness of L2 speech. Further, we compare the relevance of different feature groups modelling prosody in general (without speech tempo), speech rate and pauses modelling speech tempo (fluency), voice quality, and a variety of spectral features. We also discuss the impact of various fusion strategies on performance.Overall, our results demonstrate that the prosody of non-native speakers of English as L2 can be reliably assessed using supra- segmental audio features; prosodic features seem to be the most important ones
Spoken content retrieval: A survey of techniques and technologies
Speech media, that is, digital audio and video containing spoken content, has blossomed in recent years. Large collections are accruing on the Internet as well as in private and enterprise settings. This growth has motivated extensive research on techniques and technologies that facilitate reliable indexing and retrieval. Spoken content retrieval (SCR) requires the combination of audio and speech processing technologies with methods from information retrieval (IR). SCR research initially investigated planned speech structured in document-like units, but has subsequently shifted focus to more informal spoken content produced spontaneously, outside of the studio and in conversational settings. This survey provides an overview of the field of SCR encompassing component technologies, the relationship of SCR to text IR and automatic speech recognition and user interaction issues. It is aimed at researchers with backgrounds in speech technology or IR who are seeking deeper insight on how these fields are integrated to support research and development, thus addressing the core challenges of SCR
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