Speech Enhancement for Wake-Up-Word detection in Voice Assistants

Bonet, David; Cámbara, Guillermo; López, Fernando; Gómez, Pablo; Segura, Carlos; Luque, Jordi; Farrús, Mireia

doi:10.21437/iberspeech.2021-9

Cited by 4 publications

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“…Motivated by the performance of such models, we propose to study the application and the effects of SE modules and techniques upon the performance of a WUW detection task, extending our previous work in such matter [1]. We hypothesize that cleaning noisy speech with a dedicated SE front-end should be beneficial for a WUW detector.…”

Section: Introductionmentioning

confidence: 99%

TASE: Task-Aware Speech Enhancement for Wake-Up Word Detection in Voice Assistants

et al. 2022

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Wake-up word spotting in noisy environments is a critical task for an excellent user experience with voice assistants. Unwanted activation of the device is often due to the presence of noises coming from background conversations, TVs, or other domestic appliances. In this work, we propose the use of a speech enhancement convolutional autoencoder, coupled with on-device keyword spotting, aimed at improving the trigger word detection in noisy environments. The end-to-end system learns by optimizing a linear combination of losses: a reconstruction-based loss, both at the log-mel spectrogram and at the waveform level, as well as a specific task loss that accounts for the cross-entropy error reported along the keyword spotting detection. We experiment with several neural network classifiers and report that deeply coupling the speech enhancement together with a wake-up word detector, e.g., by jointly training them, significantly improves the performance in the noisiest conditions. Additionally, we introduce a new publicly available speech database recorded for the Telefónica’s voice assistant, Aura. The OK Aura Wake-up Word Dataset incorporates rich metadata, such as speaker demographics or room conditions, and comprises hard negative examples that were studiously selected to present different levels of phonetic similarity with respect to the trigger words “OK Aura”.

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Section: Introductionmentioning

confidence: 99%

TASE: Task-Aware Speech Enhancement for Wake-Up Word Detection in Voice Assistants

et al. 2022

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“…Neural networks have shown to be an effective solution for small-footprint keyword spotting. In specific, convolutional neural networks have been the preferred models to address this task [11,12,13,14].…”

Section: Introductionmentioning

confidence: 99%

Efficient Keyword Spotting by capturing long-range interactions with Temporal Lambda Networks

Tura¹,

Escuder²,

Diego³

et al. 2021

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Recent models based on attention mechanisms have shown unprecedented performance in the speech recognition domain. These are computational expensive and unnecessarily complex for the keyword spotting task where its main usage is in smallfootprint devices. This work explores the application of the Lambda networks, a framework for capturing long-range interactions, within this spotting task. The proposed architecture is inspired by current state-of-the-art models for keyword spotting built on residual connections. Our main contribution consists on swapping the residual blocks by temporal Lambda layers thus bypassing the expensive computation of attention maps, largely reducing the model complexity. Furthermore, the proposed Lambda network is built upon uni-dimensional convolutions which also dramatically decreases the number of floating point operations performed along the inference stage. This architecture does not only reach state-of-the-art accuracies on the Google Speech Commands dataset, but it is 85% and 65% lighter than its multi headed attention (MHAtt-RNN) and residual convolutional (Res15) counterparts, while being up to 100× faster than them. To the best of our knowledge, this is the first attempt to examine the Lambda framework within the speech domain and therefore, we unravel further research and development of future speech interfaces based on this architecture.

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“…Indeed, this trade-off between accurate detection and low computational resources is an active research field within the speech recognition domain [8]. Latter advances reported the residual temporal convolution networks as an effective candidate for small-footprint KWS and have been proposed in [9,10,11] to address this task.…”

Section: Introductionmentioning

confidence: 99%

Efficient Keyword Spotting by Capturing Long-Range Interactions with Temporal Lambda Networks

Tura

Escuder

Diego

et al. 2021

2021 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)

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Models based on attention mechanisms have shown unprecedented speech recognition performance. However, they are computationally expensive and unnecessarily complex for keyword spotting, a task targeted to small-footprint devices. This work explores the application of Lambda networks, an alternative framework for capturing long-range interactions without attention, for the keyword spotting task. We propose a novel ResNet-based model by swapping the residual blocks by temporal Lambda layers. Furthermore, the proposed architecture is built upon uni-dimensional temporal convolutions that further reduce its complexity. The presented model does not only reach state-of-the-art accuracies on the Google Speech Commands dataset, but it is 85% and 65% lighter than its Transformer-based (KWT) and convolutional (ResNet15) counterparts while being up to 100× faster. To the best of our knowledge, this is the first attempt to explore the Lambda framework within the speech domain and therefore, we unravel further research of new interfaces based on this architecture.

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Speech Enhancement for Wake-Up-Word detection in Voice Assistants

Cited by 4 publications

References 0 publications

TASE: Task-Aware Speech Enhancement for Wake-Up Word Detection in Voice Assistants

TASE: Task-Aware Speech Enhancement for Wake-Up Word Detection in Voice Assistants

Efficient Keyword Spotting by capturing long-range interactions with Temporal Lambda Networks

Efficient Keyword Spotting by Capturing Long-Range Interactions with Temporal Lambda Networks

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