A practical two-stage training strategy for multi-stream end-to-end speech recognition

The multi-stream paradigm of audio processing, in which several sources are simultaneously considered, has been an active research area for information fusion. Our previous study offered a promising direction within end-to-end automatic speech recognition, where parallel encoders aim to capture diverse information followed by a stream-level fusion based on attention mechanisms to combine the different views. However, with an increasing number of streams resulting in an increasing number of encoders, the previous approach could require substantial memory and massive amounts of parallel data for joint training. In this work, we propose a practical two-stage training scheme. Stage-1 is to train a Universal Feature Extractor (UFE), where encoder outputs are produced from a single-stream model trained with all data. Stage-2 formulates a multi-stream scheme intending to solely train the attention fusion module using the UFE features and pretrained components from Stage-1. Experiments have been conducted on two datasets, DIRHA and AMI, as a multi-stream scenario. Compared with our previous method, this strategy achieves relative word error rate reductions of 8.2--32.4%, while consistently outperforming several conventional combination methods.Comment: submitted to ICASSP 201

Neural network-based method for visual recognition of driver’s voice commands using attention mechanism

Visual speech recognition or automated lip-reading systems actively apply to speech-to-text translation. Video data proves to be useful in multimodal speech recognition systems, particularly when using acoustic data is difficult or not available at all. The main purpose of this study is to improve driver command recognition by analyzing visual information to reduce touch interaction with various vehicle systems (multimedia and navigation systems, phone calls, etc.) while driving. We propose a method of automated lip-reading the driver’s speech while driving based on a deep neural network of 3DResNet18 architecture. Using neural network architecture with bi-directional LSTM model and attention mechanism allows achieving higher recognition accuracy with a slight decrease in performance. Two different variants of neural network architectures for visual speech recognition are proposed and investigated. When using the first neural network architecture, the result of voice recognition of the driver was 77.68 %, which was lower by 5.78 % than when using the second one the accuracy of which was 83.46 %. Performance of the system which is determined by a real-time indicator RTF in the case of the first neural network architecture is equal to 0.076, and the second — RTF is 0.183 which is more than two times higher. The proposed method was tested on the data of multimodal corpus RUSAVIC recorded in the car. Results of the study can be used in systems of audio-visual speech recognition which is recommended in high noise conditions, for example, when driving a vehicle. In addition, the analysis performed allows us to choose the optimal neural network model of visual speech recognition for subsequent incorporation into the assistive system based on a mobile device

Integration of Language Models in Sequence to Sequence Optical Music Recognition Systems

El present projecte és un estudi del potencial d'integrar per mitjà de diverses tècniques un model de llenguatge a un sistema de Reconeixement Òptic de Partitures (OMR) basat en una arquitectura Sequence to Sequence. L'objectiu és millorar el rendiment del model sobre partitures manuscrites antigues, que són especialment complexes d'interpretar a causa del seu elevat grau de variabilitat i les distorsions que solen incorporar.The following project is a study of the potential of integrating a language model into a Sequence to Sequence-based Optical Music Recognition (OMR) system through various techniques. The goal is to improve the performance of the model on handwritten old music scores, whose interpretation is particularly error-prone due to their high degree of variability and distortion.El presente proyecto es un estudio del potencial de integrar por medio de varias técnicas un modelo de lenguaje a un sistema de Reconocimiento Óptico de Partituras (OMR) basado en una arquitectura Sequence to Sequence. El objetivo es mejorar el rendimiento del modelo sobre partituras manuscritas antiguas, que son especialmente complicadas de interpretar a causa de su elevado grado de variabilidad y las distorsiones que suelen incorporar