# Locale Encoding For Scalable Multilingual Keyword Spotting Models

arXiv: https://arxiv.org/abs/2302.12961 · HTML: https://mlsanity.com/paper/2302.12961/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 28 references that are in the map.
- First posted on arXiv 25 Feb 2023.

## Builds on (most cited first, 14 of 14)

- [FiLM: Visual Reasoning with a General Conditioning Layer](https://mlsanity.com/paper/1709.07871/) (2017)
- [Large-Scale Multilingual Speech Recognition with a Streaming End-to-End Model](https://mlsanity.com/paper/1909.05330/) (2019)
- [Multilingual Speech Recognition With A Single End-To-End Model](https://mlsanity.com/paper/1711.01694/) (2017)
- [Lingvo: a Modular and Scalable Framework for Sequence-to-Sequence Modeling](https://mlsanity.com/paper/1902.08295/) (2019)
- [Few-Shot Keyword Spotting in Any Language](https://mlsanity.com/paper/2104.01454/) (2021)
- [End-to-End Streaming Keyword Spotting](https://mlsanity.com/paper/1812.02802/) (2018)
- [Learning To Detect Keyword Parts And Whole By Smoothed Max Pooling](https://mlsanity.com/paper/2001.09246/) (2020)
- [Noisy student-teacher training for robust keyword spotting](https://mlsanity.com/paper/2106.01604/) (2021)
- [Efficient Implementation of the Room Simulator for Training Deep Neural Network Acoustic Models](https://mlsanity.com/paper/1712.03439/) (2017)
- [Teaching keyword spotters to spot new keywords with limited examples](https://mlsanity.com/paper/2106.02443/) (2021)
- [Production federated keyword spotting via distillation, filtering, and joint federated-centralized training](https://mlsanity.com/paper/2204.06322/) (2022)
- [Fast ASR-free and almost zero-resource keyword spotting using DTW and CNNs for humanitarian monitoring](https://mlsanity.com/paper/1806.09374/) (2018)
- [Progressive Voice Trigger Detection: Accuracy vs Latency](https://mlsanity.com/paper/2010.15446/) (2020)
- [SpeakerStew: Scaling to Many Languages with a Triaged Multilingual Text-Dependent and Text-Independent Speaker Verification System](https://mlsanity.com/paper/2104.02125/) (2021)

## Built on by (most cited first, 3 of 3)

- [Synth4Kws: Synthesized Speech for User Defined Keyword Spotting in Low Resource Environments](https://mlsanity.com/paper/2407.16840/) (2024)
- [GE2E-KWS: Generalized End-to-End Training and Evaluation for Zero-shot Keyword Spotting](https://mlsanity.com/paper/2410.16647/) (2024)
- [Personalizing Keyword Spotting with Speaker Information](https://mlsanity.com/paper/2311.03419/) (2023)

## Abstract (arXiv)

A Multilingual Keyword Spotting (KWS) system detects spokenkeywords over multiple locales. Conventional monolingual KWSapproaches do not scale well to multilingual scenarios because ofhigh development/maintenance costs and lack of resource sharing.To overcome this limit, we propose two locale-conditioned universalmodels with locale feature concatenation and feature-wise linearmodulation (FiLM). We compare these models with two baselinemethods: locale-specific monolingual KWS, and a single universalmodel trained over all data. Experiments over 10 localized languagedatasets show that locale-conditioned models substantially improveaccuracy over baseline methods across all locales in different noiseconditions.FiLMperformed the best, improving on average FRRby 61% (relative) compared to monolingual KWS models of similarsizes.