A different version was used inLeroy D. et al., "Federated learning for keyword spotting", also accepted for publication to ICASSP 2019. Please mention which version you want access to in the contact form (see below). Datasets The wake word is "Hey Snips" pronounced with no pause between...
Keyword spotting is an important research field because it plays a key role in device wake-up and the user’s experience with smart devices.
Keyword spotting is an important research field because it plays a key role in device wake-up and user interaction on smart devices. However, it is challenging to minimize errors while operating efficiently in devices with limited resources such as mobile phones. We present a broadcasted residual ...
🛎️Implementation of critical audio tasks: this toolkit contains audio functions like Automatic Speech Recognition, Text-to-Speech Synthesis, Speaker Verfication, KeyWord Spotting, Audio Classification, and Speech Translation, etc. 🔬Integration of mainstream models and datasets: the toolkit implements...
A NOVEL KEYWORD SPOTTING APPROACH IN SPEECH MINING USING WAVELET PACKET TRANSFORMATION Keyword spotting (KWS) is an innovative research area and has many applications in speech mining. Keyword spotting is the task of identifying the occurrenc... S Devi,Dr. Srinivasan 被引量: 0发表: 0年 加载更...
Keyword spotting (KWS) is an important technique for speech applications, which enables users to activate devices by speaking a keyword phrase. Although a phoneme classifier can be used for KWS, exploiting a large amount of transcribed data for automatic speech recognition (ASR), there is a misma...
Moreover, we extend our method to a spotting ensemble. In an exhaustive experimental evaluation on four widely used benchmark datasets we show that the proposed approach is able to keep up or even outperform several state-of-the-art methods for template- and learning-based keyword spotting. ...
Also, the primary focus of prior research has been to maximize the accuracywith a small memory footprint model, without explicit constraints of underlying hardware, such aslimits on number of operations per inference. In contrast, this work is more hardware-centric andtargeted towards neural network...
We validate the proposed framework on two challenging large-scale spontaneous conversational telephone speech (CTS) datasets in two different languages (English and Mandarin). Experimental results show our framework can achieve consistent and significant spotting performance gains over both the maximum ...
AI-Research-BD/Keyword-MLPofficial ↳ Quickstart in Colab 15 Tasks Edit AddRemove Keyword Spotting Datasets Speech Commands Results from the Paper Edit Ranked #9 onKeyword Spotting on Google Speech Commands(Google Speech Commands V2 35 metric) ...