A Novel Speech Recognition System for Spontaneous and Non-Native Speech

Introduction

Speech recognition technology has been rapidly advancing in recent years due to the increasing demand for more efficient and natural human-computer interaction. Speech recognition applications are now ubiquitous in our daily lives, from virtual assistants like Siri and Alexa to voice-controlled smart home devices. With the development of deep learning techniques, automatic speech recognition (ASR) has achieved remarkable progress in terms of accuracy and robustness.

Despite the significant progress made in speech recognition, there are still many challenges that need to be addressed. One of the main challenges is the recognition of spontaneous speech, which is characterized by disfluencies, hesitations, and incomplete sentences. Spontaneous speech is prevalent in natural conversations, but it is much more difficult to recognize than read speech due to the lack of structure and predictability.

Another challenge is the recognition of non-native speech, which is becoming more important in today's globalized world. Non-native speech is often characterized by differences in pronunciation, intonation, and rhythm, which can significantly degrade recognition performance.

In this paper, we present a new speech recognition system that addresses the above challenges using a combination of deep learning techniques and linguistic knowledge. Our system is designed to recognize both spontaneous and non-native speech with high accuracy and robustness. We demonstrate the effectiveness of our system on several benchmark datasets, achieving state-of-the-art performance.

The contributions of this paper are twofold. First, we propose a novel approach that combines deep learning and linguistic knowledge to improve the recognition of spontaneous and non-native speech. Second, we conduct extensive experiments on benchmark datasets to demonstrate the effectiveness of our approach.

The rest of the paper is organized as follows. In Section 2, we review the related work in speech recognition and highlight the limitations of existing approaches. In Section 3, we describe our proposed speech recognition system in detail. In Section 4, we present the experimental results and compare our approach with state-of-the-art methods. Finally, we conclude the paper in Section 5 and discuss future research directions.