Speech based human machine interaction system for home automation

Katore, Mayur; Bachute, Mrinal

doi:10.1109/ibss.2015.7456634

Cited by 18 publications

(8 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Speech recognition technology can serve as a mode of communication between the aircraft captain and robotic co-pilots [124]. Apart from the industries above, speech recognition as technology has evolved in other sectors, such as the banking and marketing industry, media industry, various workplaces, intelligent homes, e-commerce industry, and many more [125].…”

Section: Aviation Industrymentioning

confidence: 99%

Challenges and Limitations in Speech Recognition Technology: A Critical Review of Speech Signal Processing Algorithms, Tools and Systems

Basak¹,

Agrawal²,

Jena³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

View full text Add to dashboard Cite

Speech recognition systems have become a unique human-computer interaction (HCI) family. Speech is one of the most naturally developed human abilities; speech signal processing opens up a transparent and hand-free computation experience. This paper aims to present a retrospective yet modern approach to the world of speech recognition systems. The development journey of ASR (Automatic Speech Recognition) has seen quite a few milestones and breakthrough technologies that have been highlighted in this paper. A step-by-step rundown of the fundamental stages in developing speech recognition systems has been presented, along with a brief discussion of various modern-day developments and applications in this domain. This review paper aims to summarize and provide a beginning point for those starting in the vast field of speech signal processing. Since speech recognition has a vast potential in various industries like telecommunication, emotion recognition, healthcare, etc., this review would be helpful to researchers who aim at exploring more applications that society can quickly adopt in future years of evolution. KEYWORDSSpeech recognition; automatic speech recognition (ASR); mel-frequency cepstral coefficients (MFCC); hidden Markov model (HMM); artificial neural network (ANN)

show abstract

Section: Aviation Industrymentioning

confidence: 99%

Challenges and Limitations in Speech Recognition Technology: A Critical Review of Speech Signal Processing Algorithms, Tools and Systems

Basak¹,

Agrawal²,

Jena³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

View full text Add to dashboard Cite

show abstract

“…Automatic speech recognition (ASR) is a common speech technology in people's daily life that can recognize the speaker's intention, while its development has greatly promoted the relationship between humans and computers, enabling computers to better understand human language for natural and robust human-computer interaction. Common applications include digital personal care [1], smart home [2], smart medical [3], [4], etc. Although ASR has achieved great success, its use in some special scenarios is still limited.…”

Section: Introductionmentioning

confidence: 99%

Decoding Silent Speech Based on High-Density Surface Electromyogram Using Spatiotemporal Neural Network

Chen

Zhang

Chen

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

View full text Add to dashboard Cite

Finer-grained decoding at a phoneme or syllable level is a key technology for continuous recognition of silent speech based on surface electromyogram (sEMG). This paper aims at developing a novel syllable-level decoding method for continuous silent speech recognition (SSR) using spatio-temporal end-to-end neural network. In the proposed method, the high-density sEMG (HD-sEMG) was first converted into a series of feature images, and then a spatio-temporal end-to-end neural network was applied to extract discriminative feature representations and to achieve syllable-level decoding. The effectiveness of the proposed method was verified with HD-sEMG data recorded by four pieces of 64-channel electrode arrays placed over facial and laryngeal muscles of fifteen subjects subvocalizing 33 Chinese phrases consisting of 82 syllables. The proposed method outperformed the benchmark methods by achieving the highest phrase classification accuracy (97.17 ± 1.53%, p < 0.05), and lower character error rate (3.11 ± 1.46%, p < 0.05). This study provides a promising way of decoding sEMG towards SSR, which has great potential applications in instant communication and remote control.

show abstract

“…Speech has always been the best way for humans to communicate, but making it effective while dealing with technology has always been a difficulty. The major difficulty in identifying speech is that each person has a distinct manner of speaking, accent, pronunciation, pitch, rhythm, emotional state, and so on, and there are variances even in physical and linguistic features (1). The majority of commercial ASR systems available on adult speech are working efficiently.…”

Section: Introductionmentioning

confidence: 99%

Challenges for Designing of Children Speech Corpora: A State-of-the-Art Review

Sobti¹,

Kadyan²,

Guleria³

2022

ECS Trans.

View full text Add to dashboard Cite

Automatic Speech Recognition (ASR) is the use of computer hardware and software-based techniques to identify and process human voices. These systems used data from both male and female speakers. The majority of commercial ASR systems available on adult speech are working efficiently. Speech data collected from both male and female speakers were used in these systems. In recent decades, ASR systems for children have emerged, such as reading tutors, aids for foreign language learning, and computer games. Child ASR systems are essential but poorly understood in the field of computer speech recognition. The child data collection is a very complex task. Child corpus is not available publicly, and variability of children speakers and ASR developed for a particular age group is not suitable for other age groups. These are some of the reasons for less and ineffective child ASR systems. However, the non-availability of child corpus publicly is a primary reason for ineffective child ASR. Designing and developing child corpus is a very tedious task. Therefore, the primary focus of this state-of-the-art review is to discuss various challenges encountered while designing and developing child corpus.

show abstract

Speech based human machine interaction system for home automation

Cited by 18 publications

References 14 publications

Challenges and Limitations in Speech Recognition Technology: A Critical Review of Speech Signal Processing Algorithms, Tools and Systems

Challenges and Limitations in Speech Recognition Technology: A Critical Review of Speech Signal Processing Algorithms, Tools and Systems

Decoding Silent Speech Based on High-Density Surface Electromyogram Using Spatiotemporal Neural Network

Challenges for Designing of Children Speech Corpora: A State-of-the-Art Review

Contact Info

Product

Resources

About