System request detection in human conversation based on multi-resolution Gabor wavelet features

Yamagata, Tomoyuki; Takiguchi, Tetsuya; Ariki, Yasuo

doi:10.21437/interspeech.2009-89

Cited by 8 publications

References 16 publications

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Discriminate Natural versus Loudspeaker Emitted Speech

Gilberton

Duong

2019

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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In this work, we address a novel, but potentially emerging, problem of discriminating the natural human voices and those played back by any kind of audio devices in the context of interactions with in-house voice user interface. The tackled problem may find relevant applications in (1) the far-field voice interactions of vocal interfaces such as Amazon Echo, Google Home, Facebook Portal, etc, and (2) the replay spoofing attack detection. The detection of loudspeaker emitted speech will help avoiding false wake-ups or unintended interactions with the devices in the first application, while eliminating attacks involve the replay of recordings collected from enrolled speakers in the second one. At first we collect a real-world dataset under well-controlled conditions containing two classes: recorded speeches directly spoken by numerous people (considered as the natural speech), and recorded speeches played back from various loudspeakers (considered as the loudspeaker emitted speech). Then from this dataset, we build prediction models based on Deep Neural Network (DNN) for which different combination of audio features have been considered. Experiment results confirm the feasibility of the task where the combination of audio embeddings extracted from SoundNet and VGGish network yields the classification accuracy up to about 90%.Index Terms-Natural versus device emitted speech classification, voice interaction, audio features, deep neural network (DNN).

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Discriminate Natural versus Loudspeaker Emitted Speech

Gilberton

Duong

2019

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Improving Identification of System-Directed Speech Utterances by Deep Learning of ASR-Based Word Embeddings and Confidence Metrics

Vilaysouk

Nour-Eldin

Connolly

2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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In this paper, we extend our previous work on the detection of system-directed speech utterances. This type of binary classification can be used by virtual assistants to create a more natural and fluid interaction between the system and the user. We explore two methods that both improve the Equal-Error-Rate (EER) performance of the previous model. The first exploits the supplementary information independently captured by ASR models through integrating ASR decoder-based features as additional inputs to the final classification stage of the model. This relatively improves EER performance by 13%. The second proposed method further integrates word embeddings into the architecture and, when combined with the first method, achieves a significant EER performance improvement of 48%, relative to that of the baseline.

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Streaming ResLSTM with Causal Mean Aggregation for Device-Directed Utterance Detection

Tong

Huang

Mallidi

et al. 2021

2021 IEEE Spoken Language Technology Workshop (SLT)

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In this paper, we propose a streaming model to distinguish voice queries intended for a smart-home device from background speech. The proposed model consists of multiple CNN layers with residual connections, followed by a stacked LSTM architecture. The streaming capability is achieved by using unidirectional LSTM layers and a causal mean aggregation layer to form the final utterance-level prediction up to the current frame. In order to avoid redundant computation during online streaming inference, we use a caching mechanism for every convolution operation. Experimental results on a device-directed vs. non device-directed task show that the proposed model yields an equal error rate reduction of 41% compared to our previous best model on this task. Furthermore, we show that the proposed model is able to accurately predict earlier in time compared to the attention-based models.

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System request detection in human conversation based on multi-resolution Gabor wavelet features

Cited by 8 publications

References 16 publications

Discriminate Natural versus Loudspeaker Emitted Speech

Discriminate Natural versus Loudspeaker Emitted Speech

Improving Identification of System-Directed Speech Utterances by Deep Learning of ASR-Based Word Embeddings and Confidence Metrics

Streaming ResLSTM with Causal Mean Aggregation for Device-Directed Utterance Detection

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