Attention-enhanced connectionist temporal classification for discrete speech emotion recognition

Discrete speech emotion recognition (SER), the assignment of a single emotion label to an entire speech utterance, is typically performed as a sequence-to-label task. This approach, however, is limited, in that it can result in models that do not capture temporal changes in the speech signal, including those indicative of a particular emotion. One potential solution to overcome this limitation is to model SER as a sequence-to-sequence task instead. In this regard, we have developed an attention-based bidirectional long short-term memory (BLSTM) neural network in combination with a connectionist temporal classification (CTC) objective function (Attention-BLSTM-CTC) for SER. We also assessed the benefits of incorporating two contemporary attention mechanisms, namely component attention and quantum attention, into the CTC framework. To the best of the authors’ knowledge, this is the first time that such a hybrid architecture has been employed for SER.We demonstrated the effectiveness of our approach on the Interactive Emotional Dyadic Motion Capture (IEMOCAP) and FAU-Aibo Emotion corpora. The experimental results demonstrate that our proposed model outperforms current state-of-the-art approaches.The work presented in this paper substantially supported by the National Natural Science Foundation of China (Grant No. 61702370), the Key Program of the Natural Science Foundation of Tianjin (Grant No. 18JCZDJC36300), the Open Projects Program of the National Laboratory of Pattern Recognition, and the Senior Visiting Scholar Program of Tianjin Normal University. Interspeech 2019 ISSN: 1990-977

Disentangling Prosody Representations with Unsupervised Speech Reconstruction

Human speech can be characterized by different components, including semantic content, speaker identity and prosodic information. Significant progress has been made in disentangling representations for semantic content and speaker identity in Automatic Speech Recognition (ASR) and speaker verification tasks respectively. However, it is still an open challenging research question to extract prosodic information because of the intrinsic association of different attributes, such as timbre and rhythm, and because of the need for supervised training schemes to achieve robust large-scale and speaker-independent ASR. The aim of this paper is to address the disentanglement of emotional prosody from speech based on unsupervised reconstruction. Specifically, we identify, design, implement and integrate three crucial components in our proposed speech reconstruction model Prosody2Vec: (1) a unit encoder that transforms speech signals into discrete units for semantic content, (2) a pretrained speaker verification model to generate speaker identity embeddings, and (3) a trainable prosody encoder to learn prosody representations. We first pretrain the Prosody2Vec representations on unlabelled emotional speech corpora, then fine-tune the model on specific datasets to perform Speech Emotion Recognition (SER) and Emotional Voice Conversion (EVC) tasks. Both objective (weighted and unweighted accuracies) and subjective (mean opinion score) evaluations on the EVC task suggest that Prosody2Vec effectively captures general prosodic features that can be smoothly transferred to other emotional speech. In addition, our SER experiments on the IEMOCAP dataset reveal that the prosody features learned by Prosody2Vec are complementary and beneficial for the performance of widely used speech pretraining models and surpass the state-of-the-art methods when combining Prosody2Vec with HuBERT representations.Comment: Accepted by IEEE/ACM Transactions on Audio, Speech, and Language Processin

Emotional Storyteller for Vision Impaired and Hearing-Impaired Children

Tellie is an innovative mobile app designed to offer an immersive and emotionally enriched storytelling experience for children who are visually and hearing impaired. It achieves this through four main objectives: Text extraction utilizes the CRAFT model and a combination of Convolutional Neural Networks (CNNs), Connectionist Temporal Classification (CTC), and Long Short-Term Memory (LSTM) networks to accurately extract and recognize text from images in storybooks. Recognition of Emotions in Sentences employs BERT to detect and distinguish emotions at the sentence level including happiness, anger, sadness, and surprise. Conversion of Text to Human Natural Audio with Emotion transforms text into emotionally expressive audio using Tacotron2 and Wave Glow, enhancing the synthesized speech with emotional styles to create engaging audio narratives. Conversion of Text to Sign Language: To cater to the Deaf and hard-of-hearing community, Tellie translates text into sign language using CNNs, ensuring alignment with real sign language expressions. These objectives combine to create Tellie, a groundbreaking app that empowers visually and hearing-impaired children with access to captivating storytelling experiences, promoting accessibility and inclusivity through the harmonious integration of language, creativity, and technology. This research demonstrates the potential of advanced technologies in fostering inclusive and emotionally engaging storytelling for all children

A Review of Deep Learning Techniques for Speech Processing

The field of speech processing has undergone a transformative shift with the advent of deep learning. The use of multiple processing layers has enabled the creation of models capable of extracting intricate features from speech data. This development has paved the way for unparalleled advancements in speech recognition, text-to-speech synthesis, automatic speech recognition, and emotion recognition, propelling the performance of these tasks to unprecedented heights. The power of deep learning techniques has opened up new avenues for research and innovation in the field of speech processing, with far-reaching implications for a range of industries and applications. This review paper provides a comprehensive overview of the key deep learning models and their applications in speech-processing tasks. We begin by tracing the evolution of speech processing research, from early approaches, such as MFCC and HMM, to more recent advances in deep learning architectures, such as CNNs, RNNs, transformers, conformers, and diffusion models. We categorize the approaches and compare their strengths and weaknesses for solving speech-processing tasks. Furthermore, we extensively cover various speech-processing tasks, datasets, and benchmarks used in the literature and describe how different deep-learning networks have been utilized to tackle these tasks. Additionally, we discuss the challenges and future directions of deep learning in speech processing, including the need for more parameter-efficient, interpretable models and the potential of deep learning for multimodal speech processing. By examining the field's evolution, comparing and contrasting different approaches, and highlighting future directions and challenges, we hope to inspire further research in this exciting and rapidly advancing field

Survey of deep representation learning for speech emotion recognition

Traditionally, speech emotion recognition (SER) research has relied on manually handcrafted acoustic features using feature engineering. However, the design of handcrafted features for complex SER tasks requires significant manual eort, which impedes generalisability and slows the pace of innovation. This has motivated the adoption of representation learning techniques that can automatically learn an intermediate representation of the input signal without any manual feature engineering. Representation learning has led to improved SER performance and enabled rapid innovation. Its effectiveness has further increased with advances in deep learning (DL), which has facilitated \textit{deep representation learning} where hierarchical representations are automatically learned in a data-driven manner. This paper presents the first comprehensive survey on the important topic of deep representation learning for SER. We highlight various techniques, related challenges and identify important future areas of research. Our survey bridges the gap in the literature since existing surveys either focus on SER with hand-engineered features or representation learning in the general setting without focusing on SER