Time-Delayed Bottleneck Highway Networks Using a DFT Feature for Keyword Spotting

Guo, Jianmin; Kumatani, Kenichi; Sun, Ming; Wu, Minhua; Raju, Anirudh; Ström, Nikko; Mandal, Arindam

doi:10.1109/icassp.2018.8462166

Cited by 42 publications

(33 citation statements)

References 12 publications

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“…Eqs. (3) to (7) (with the number of positive targets n=1) and (8) to (9) define the loss for the decoder submodel. ] includes actual end-point of the keyword.…”

Section: Smoothed Max Pooling Loss For Decodermentioning

confidence: 99%

Learning to Detect Keyword Parts and Whole by Smoothed Max Pooling

Park

Violette

Subrahmanya

2020

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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We propose smoothed max pooling loss and its application to keyword spotting systems. The proposed approach jointly trains an encoder (to detect keyword parts) and a decoder (to detect whole keyword) in a semi-supervised manner. The proposed new loss function allows training a model to detect parts and whole of a keyword, without strictly depending on frame-level labeling from LVCSR (Large vocabulary continuous speech recognition), making further optimization possible. The proposed system outperforms the baseline keyword spotting model in [1] due to increased optimizability. Further, it can be more easily adapted for on-device learning applications due to reduced dependency on LVCSR.

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“…Eqs. (3) to (7) (with the number of positive targets n=1) and (8) to (9) define the loss for the decoder submodel. ] includes actual end-point of the keyword.…”

Section: Smoothed Max Pooling Loss For Decodermentioning

confidence: 99%

Learning to Detect Keyword Parts and Whole by Smoothed Max Pooling

Park

Violette

Subrahmanya

2020

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…We use 993 utterances from the data. 'Hey Snapdragon' utterances are from a publicly available dataset 1 . There are 50 speakers and each of them speaks the keyword 22 or 23 times.…”

Section: Query and Testing Datamentioning

confidence: 99%

“…They collect numerous variations of a specific keyword utterance and train neural networks (NNs) which have been promising method in the field. [1,2] have acoustic encoder and sequence matching decoder as separate modules. The NN-based acoustic models (AMs) predict senone-level Qualcomm AI Research is an initiative of Qualcomm Technologies, Inc posteriors.…”

Section: Introductionmentioning

confidence: 99%

Query-by-Example On-Device Keyword Spotting

Kim¹,

Lee²,

Lee³

et al. 2019

2019 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)

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A keyword spotting (KWS) system determines the existence of, usually predefined, keyword in a continuous speech stream. This paper presents a query-by-example on-device KWS system which is user-specific. The proposed system consists of two main steps: query enrollment and testing. In query enrollment step, phonetic posteriors are output by a small-footprint automatic speech recognition model based on connectionist temporal classification. Using the phoneticlevel posteriorgram, hypothesis graph of finite-state transducer (FST) is built, thus can enroll any keywords thus avoiding an out-of-vocabulary problem. In testing, a log-likelihood is scored for input audio using the FST. We propose a threshold prediction method while using the user-specific keyword hypothesis only. The system generates query-specific negatives by rearranging each query utterance in waveform. The threshold is decided based on the enrollment queries and generated negatives. We tested two keywords in English, and the proposed work shows promising performance while preserving simplicity.

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“…Prior work on FTM mainly comprises of research on key-word spotting and wake-up word detection. VT detection approaches typically rely on multi-stage neural network based processing of acoustic features to determine the presence of the wake-word [1,3,4,5,6]. These approaches often use ASR as an auxiliary task to aid the VT detection task.…”

Section: Introductionmentioning

confidence: 99%

Lattice-Based Improvements for Voice Triggering Using Graph Neural Networks

Dighe

Adya

et al. 2020

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Voice-triggered smart assistants often rely on detection of a triggerphrase before they start listening for the user request. Mitigation of false triggers is an important aspect of building a privacy-centric non-intrusive smart assistant. In this paper, we address the task of false trigger mitigation (FTM) using a novel approach based on analyzing automatic speech recognition (ASR) lattices using graph neural networks (GNN). The proposed approach uses the fact that decoding lattice of a falsely triggered audio exhibits uncertainties in terms of many alternative paths and unexpected words on the lattice arcs as compared to the lattice of a correctly triggered audio. A pure trigger-phrase detector model doesn't fully utilize the intent of the user speech whereas by using the complete decoding lattice of user audio, we can effectively mitigate speech not intended for the smart assistant. We deploy two variants of GNNs in this paper based on 1) graph convolution layers and 2) self-attention mechanism respectively. Our experiments demonstrate that GNNs are highly accurate in FTM task by mitigating ∼87% of false triggers at 99% true positive rate (TPR). Furthermore, the proposed models are fast to train and efficient in parameter requirements.

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Time-Delayed Bottleneck Highway Networks Using a DFT Feature for Keyword Spotting

Cited by 42 publications

References 12 publications

Learning to Detect Keyword Parts and Whole by Smoothed Max Pooling

Learning to Detect Keyword Parts and Whole by Smoothed Max Pooling

Query-by-Example On-Device Keyword Spotting

Lattice-Based Improvements for Voice Triggering Using Graph Neural Networks

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