Contextual Language Model Adaptation for Conversational Agents

By Anirudh Raju, Behnam Hedayatnia, Linda Liu, Ankur Gandhe, Chandra Khatri, Angeliki Metallinou, Anu Venkatesh, A. Rastrow. 2018

26 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence

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Citations in the corpus

Agnostic Federated Learning (2019, methodology): “This also includes previous applications in natural language processing (Dredze et al., 2007; Blitzer et al., 2007; Jiang and Zhai, 2007; Raju et al., 2018), speech recognition (Legetter and Woodland, 1995; Gauvain and Chin-Hui, 1994; Pietra et al., 1992; Rosenfeld, 1996; Jelinek, 1998; Roark and…”
Speech Processing for Digital Home Assistants: Combining signal processing with deep-learning techniques (2019, methodology): “119 IEEE SIGNAL PROCESSING MAGAZINE | November 2019 | used in [41] does an on-the-fly adjustment of the weights in an LM interpolation.”