# Wake Word Detection with Alignment-Free Lattice-Free MMI

arXiv: https://arxiv.org/abs/2005.08347 · HTML: https://mlsanity.com/paper/2005.08347/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 May 2020.

## Built on by (most cited first, 6 of 6)

- [Wake Word Detection with Streaming Transformers](https://mlsanity.com/paper/2102.04488/) (2021)
- [Spoken Term Detection and Relevance Score Estimation using Dot-Product of Pronunciation Embeddings](https://mlsanity.com/paper/2210.11895/) (2022)
- [Low-resource Low-footprint Wake-word Detection using Knowledge Distillation](https://mlsanity.com/paper/2207.03331/) (2022)
- [Auto-KWS 2021 Challenge: Task, Datasets, and Baselines](https://mlsanity.com/paper/2104.00513/) (2021)
- [To Wake-up or Not to Wake-up: Reducing Keyword False Alarm by Successive Refinement](https://mlsanity.com/paper/2304.03416/) (2023)
- [Handling the Alignment for Wake Word Detection: A Comparison Between Alignment-Based, Alignment-Free and Hybrid Approaches](https://mlsanity.com/paper/2302.08950/) (2023)

## Abstract (arXiv)

Always-on spoken language interfaces, e.g. personal digital assistants, rely on a wake word to start processing spoken input. We present novel methods to train a hybrid DNN/HMM wake word detection system from partially labeled training data, and to use it in on-line applications: (i) we remove the prerequisite of frame-level alignments in the LF-MMI training algorithm, permitting the use of un-transcribed training examples that are annotated only for the presence/absence of the wake word; (ii) we show that the classical keyword/filler model must be supplemented with an explicit non-speech (silence) model for good performance; (iii) we present an FST-based decoder to perform online detection. We evaluate our methods on two real data sets, showing 50%--90% reduction in false rejection rates at pre-specified false alarm rates over the best previously published figures, and re-validate them on a third (large) data set.