Multi-Keyword Model
A speech keyword recognition model that outputs an independent score for each of several preset keywords, commonly using sigmoid multi-label outputs together with threshold and cooldown logic for disambiguation. It is suited to scenarios with a fixed command set.
Timeline (last 90 days)
In offline voice control, wake word detection and command recognition are split into a two-stage architecture: the audio stream is continuously fed to the wake word model first, and once the wake word is hit a brief time window opens, with the audio within that window handed to a multi-keyword model for command recognition.
The output layer of a multi-keyword model outputs an independent score for each keyword label (commonly a sigmoid multi-label output), and the same audio frame can theoretically activate multiple labels at once, so applications need to combine thresholds with cooldown logic to disambiguate.
Multi-keyword models are suitable for scenarios where the vocabulary is known in advance and cannot replace open-ended speech recognition; utterances containing arbitrary parameters (such as "set the living room light to 37%") still require ASR plus intent parsing.
A multi-keyword model can address scenarios where a product needs a wake word plus multiple commands, exposing multiple outputs through a single inference pipeline rather than loading a separate model for each phrase.
All Facts (4)
In offline voice control, wake word detection and command recognition are split into a two-stage architecture: the audio stream is continuously fed to the wake word model first, and once the wake word is hit a brief time window opens, with the audio within that window handed to a multi-keyword model for command recognition.
50%UnverifiedThe output layer of a multi-keyword model outputs an independent score for each keyword label (commonly a sigmoid multi-label output), and the same audio frame can theoretically activate multiple labels at once, so applications need to combine thresholds with cooldown logic to disambiguate.
50%UnverifiedMulti-keyword models are suitable for scenarios where the vocabulary is known in advance and cannot replace open-ended speech recognition; utterances containing arbitrary parameters (such as "set the living room light to 37%") still require ASR plus intent parsing.
50%UnverifiedA multi-keyword model can address scenarios where a product needs a wake word plus multiple commands, exposing multiple outputs through a single inference pipeline rather than loading a separate model for each phrase.
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