Contextual Language Model Adaptation for Conversational Agents

Abstract

Statistical language models (LM) play a key role in Automatic Speech Recognition (ASR) systems used by conversational agents. These ASR systems should provide a high accuracy under a variety of speaking styles, domains, vocabulary and argots. In this paper, we present a DNN-based method to adapt the LM to each user-agent interaction based on generalized contextual information, by predicting an optimal, context-dependent set of LM interpolation weights. We show that this framework for contextual adaptation provides accuracy improvements under different possible mixture LM partitions that are relevant for both (1) Goal-oriented conversational agents where it's natural to partition the data by the requested application and for (2) Non-goal oriented conversational agents where the data can be partitioned using topic labels that come from predictions of a topic classifier. We obtain a relative WER improvement of 3% with a 1-pass decoding strategy and 6% in a 2-pass decoding framework, over an unadapted model. We also show up to a 15% relative improvement in recognizing named entities which is of significant value for conversational ASR systems.

arXiv commentInterspeech 2018 (accepted)

References in corpus (1)

• Topic-based Evaluation for Conversational Bots

Cited by in corpus (8)

• Agnostic Federated Learning
• Advancing the State of the Art in Open Domain Dialog Systems through the Alexa Prize
• Adapting Long Context NLM for ASR Rescoring in Conversational Agents
• LLM-based speaker diarization correction: A generalizable approach
• Personalized Query Rewriting in Conversational AI Agents
• Tree-constrained Pointer Generator for End-to-end Contextual Speech Recognition
• Domain-aware Neural Language Models for Speech Recognition
• Contextual Biasing of Language Models for Speech Recognition in Goal-Oriented Conversational Agents