Articulation index-based acoustic signal processing for enhanced speech intelligibility - Patil, Mahesh Shankarrao; Varadarajan, Vijaykumar; Sarode, Harsha Jitendra; Sayyad, Farook; Sarawale, Rahul Krishna; Sayyad, Shabnam; Dewi, Deshinta Arrova (Sound & Vibration, 2026)
This paper presents an innovative approach to improving speech intelligibility using the wavelet transform and the Articulation Index (AI) as an objective evaluation metric. Conventional methods such as the Modified Rhyme Test (MRT) and Mean Opinion Score (MOS) rely on subjective assessment, making them time-consuming and difficult to standardize. In contrast, AI provides a consistent and reliable measure of speech intelligibility across varying noise conditions. The proposed method applies wavelet packet transform to noisy speech signals, followed by a thresholding function to enhance signal quality and intelligibility. The processed speech is then reconstructed using the inverse wavelet transform. Experiments are conducted using the Noiseus database, which contains speech signals corrupted by real-world noises such as streets, airports, cockpits, and industrial environments like mechanical factories, with noise levels ranging from 0 dB to 15 dB. Three different enhancement methods are implemented, with the proposed method demonstrating superior performance in terms of AI values. Experimental validation is supported by plots and spectrograms, highlighting its effectiveness over existing approaches. The method leverages the multi-resolution property of the wavelet transform to preserve temporal characteristics while reducing noise across multiple frequency bands. Results show a significant improvement in AI values, indicating enhanced speech intelligibility under diverse noise conditions. This work contributes to acoustic speech enhancement by providing a robust, objective framework suitable for applications in noisy environments such as industrial communication systems, and this technique aligns closely with noise mitigation approaches used in structural and industrial surroundings. Additionally, the approach can be extended to industrial speech enhancement and environmental noise control.