CALM: Contrastive Aligned Audio-Language Multirate and Multimodal Representations

TL;DR

CALM introduces a contrastive, multirate approach to learn aligned audio and language representations, achieving competitive performance in emotion recognition with efficient training.

Contribution

The paper presents CALM, a novel method for aligning audio and lexical representations using contrastive learning and multirate processing within a pretrained language model.

Findings

Achieves 10-25% improvement in emotion recognition accuracy.

Aligns audio and language embeddings effectively with minimal training time.

Demonstrates benefits of multirate pretraining for multimodal tasks.

Abstract

Deriving multimodal representations of audio and lexical inputs is a central problem in Natural Language Understanding (NLU). In this paper, we present Contrastive Aligned Audio-Language Multirate and Multimodal Representations (CALM), an approach for learning multimodal representations using contrastive and multirate information inherent in audio and lexical inputs. The proposed model aligns acoustic and lexical information in the input embedding space of a pretrained language-only contextual embedding model. By aligning audio representations to pretrained language representations and utilizing contrastive information between acoustic inputs, CALM is able to bootstrap audio embedding competitive with existing audio representation models in only a few hours of training time. Operationally, audio spectrograms are processed using linearized patches through a Spectral Transformer (SpecTran) which is trained using a Contrastive Audio-Language Pretraining objective to align audio and language from similar queries. Subsequently, the derived acoustic and lexical tokens representations are input into a multimodal transformer to incorporate utterance level context and derive the proposed CALM representations. We show that these pretrained embeddings can subsequently be used in multimodal supervised tasks and demonstrate the benefits of the proposed pretraining steps in terms of the alignment of the two embedding spaces and the multirate nature of the pretraining. Our system shows 10-25\% improvement over existing emotion recognition systems including state-of-the-art three-modality systems under various evaluation objectives.