A Hybrid Speech Enhancement Algorithm for Voice Assistance Application

Abstract

In recent years, speech recognition technology has become a more common notion. Speech quality and intelligibility are critical for the convenience and accuracy of information transmission in speech recognition. The speech processing systems used to converse or store speech are usually designed for an environment without any background noise. However, in a real-world atmosphere, background intervention in the form of background noise and channel noise drastically reduces the performance of speech recognition systems, resulting in imprecise information transfer and exhausting the listener. When communication systems’ input or output signals are affected by noise, speech enhancement techniques try to improve their performance. To ensure the correctness of the text produced from speech, it is necessary to reduce the external noises involved in the speech audio. Reducing the external noise in audio is difficult as the speech can be of single, continuous or spontaneous words. In automatic speech recognition, there are various typical speech enhancement algorithms available that have gained considerable attention. However, these enhancement algorithms work well in simple and continuous audio signals only. Thus, in this study, a hybridized speech recognition algorithm to enhance the speech recognition accuracy is proposed. Non-linear spectral subtraction, a well-known speech enhancement algorithm, is optimized with the Hidden Markov Model and tested with 6660 medical speech transcription audio files and 1440 Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) audio files. The performance of the proposed model is compared with those of various typical speech enhancement algorithms, such as iterative signal enhancement algorithm, subspace-based speech enhancement, and non-linear spectral subtraction. The proposed cascaded hybrid algorithm was found to achieve a minimum word error rate of 9.5% and 7.6% for medical speech and RAVDESS speech, respectively. The cascading of the speech enhancement and speech-to-text conversion architectures results in higher accuracy for enhanced speech recognition. The evaluation results confirm the incorporation of the proposed method with real-time automatic speech recognition medical applications where the complexity of terms involved is high.

An Experimental Study on Speech Enhancement Based on a Combination of Wavelets and Deep Learning

“…Other hybrid or cascade approaches have been tested recently in similar domains, for example, speech emotion recognition [53]. The results of this study may represent an opportunity to develop more hybrid approaches, where the benefit of each stage can be analyzed separately, in a similar way to image enhancement, where different algorithms to enhance aspects such as noise, blur, and compression have been applied separately, using a cascade approach.…”

Harris Hawks Sparse Auto-Encoder Networks for Automatic Speech Recognition System

“…The ASR system has minimum accuracy when it receives information from speech based on loud and background noise [10,11]. Noise is important for malware attacks in Image-based classification [12].…”

Ensemble Machine Learning-Based Approach for Predicting of FRP–Concrete Interfacial Bonding

“…Integrating additional new machine learning algorithms in series would help fix the prediction errors made by previous models. The advancements in the usage of machine learning algorithms across different domains allow the researchers to provide artificial intelligence-based solutions for various real-time problems [36][37][38]. An experimental study on the evaluation of a variety of machine learning models for the bond strength prediction was conducted.…”