Synth4Kws: Synthesized Speech for User Defined Keyword Spotting in Low Resource Environments
One of the challenges in developing a high quality custom keyword spotting (KWS) model is the lengthy and expensive process of collecting training data covering a wide range of languages, phrases and speaking styles. We introduce Synth4Kws - a framework to leverage Text to Speech (TTS) synthesized data for custom KWS in different resource settings. With no real data, we found increasing TTS phrase diversity and utterance sampling monotonically improves model performance, as evaluated by EER and AUC metrics over 11k utterances of the speech command dataset. In low resource settings, with 50k real utterances as a baseline, we found using optimal amounts of TTS data can improve EER by 30.1% and AUC by 46.7%. Furthermore, we mix TTS data with varying amounts of real data and interpolate the real data needed to achieve various quality targets. Our experiments are based on English and single word utterances but the findings generalize to i18n languages and other keyword types.
Code (0)
등록된 구현이 없습니다.
Tasks
DiversityKeyword Spottingtext-to-speechText to SpeechSimilar Papers 제목 키워드 기반
LLM-Synth4KWS: Scalable Automatic Generation and Synthesis of Confusable Data for Custom Keyword Spotting
Custom keyword spotting (KWS) allows detecting user-defined spoken keywords from streaming audio. This is achieved by comparing the embeddings from voice enrollments and input audio. State-of-the-art custom KWS models ar…
Keyword Spottingtext-to-speechText to SpeechTraining Wake Word Detection with Synthesized Speech Data on Confusion Words
Confusing-words are commonly encountered in real-life keyword spotting applications, which causes severe degradation of performance due to complex spoken terms and various kinds of words that sound similar to the predefi…
Data AugmentationKeyword SpottingSpeech Synthesistext-to-speech+1Training Keyword Spotters with Limited and Synthesized Speech Data
With the rise of low power speech-enabled devices, there is a growing demand to quickly produce models for recognizing arbitrary sets of keywords. As with many machine learning tasks, one of the most challenging parts in…
Keyword SpottingMetric Learning for User-defined Keyword Spotting
The goal of this work is to detect new spoken terms defined by users. While most previous works address Keyword Spotting (KWS) as a closed-set classification problem, this limits their transferability to unseen terms. Th…
Keyword SpottingMetric LearningFew-Shot Keyword Spotting With Prototypical Networks
Recognizing a particular command or a keyword, keyword spotting has been widely used in many voice interfaces such as Amazon's Alexa and Google Home. In order to recognize a set of keywords, most of the recent deep learn…
Keyword SpottingMetric Learning