Individual Variability in Recognition of Frequency-Lowered Speech
نویسنده
چکیده
Frequency lowering in hearing aids is not a new concept, but modern advances in technology have allowed it to be performed more efficiently and on select portions of the spectrum. Nonlinear frequency compression reduces the frequency spacing in a band of high-frequency energy so that more information is carried in the audible bandwidth. Frequency transposition and translation techniques lower only the part of the high-frequency spectrum that likely contains important speech information. These advances may help overcome the limited bandwidth in conventional hearing aids, which restrict access to high-frequency information even for those with mild to moderate hearing loss. This is especially important for young children learning speech and language. A framework is advanced in which factors that influence individual differences in speech recognition can be divided into extrinsic factors that affect the representation of the frequency-lowered speech at the auditory periphery, including the specific technique and the settings chosen for it, and intrinsic factors that contribute to an individual’s ability to learn and benefit from this signal. Finally, the importance of electroacoustically verifying the output to avoid too little or too much lowering and the importance of validating effectiveness of outcomes in individual users of the technology are emphasized.
منابع مشابه
Correlation between Auditory Spectral Resolution and Speech Perception in Children with Cochlear Implants
Background: Variability in speech performance is a major concern for children with cochlear implants (CIs). Spectral resolution is an important acoustic component in speech perception. Considerable variability and limitations of spectral resolution in children with CIs may lead to individual differences in speech performance. The aim of this study was to assess the correlation between auditory ...
متن کاملPersian Phone Recognition Using Acoustic Landmarks and Neural Network-based variability compensation methods
Speech recognition is a subfield of artificial intelligence that develops technologies to convert speech utterance into transcription. So far, various methods such as hidden Markov models and artificial neural networks have been used to develop speech recognition systems. In most of these systems, the speech signal frames are processed uniformly, while the information is not evenly distributed ...
متن کاملVoice-based Age and Gender Recognition using Training Generative Sparse Model
Abstract: Gender recognition and age detection are important problems in telephone speech processing to investigate the identity of an individual using voice characteristics. In this paper a new gender and age recognition system is introduced based on generative incoherent models learned using sparse non-negative matrix factorization and atom correction post-processing method. Similar to genera...
متن کاملStatistical Variation Analysis of Formant and Pitch Frequencies in Anger and Happiness Emotional Sentences in Farsi Language
Setup of an emotion recognition or emotional speech recognition system is directly related to how emotion changes the speech features. In this research, the influence of emotion on the anger and happiness was evaluated and the results were compared with the neutral speech. So the pitch frequency and the first three formant frequencies were used. The experimental results showed that there are lo...
متن کاملAn Analysis of Individual Differences in Recognizing Monosyllabic Words Under the Speech Intelligibility Index Framework
Individual differences in the recognition of monosyllabic words, either in isolation (NU6 test) or in sentence context (SPIN test), were investigated under the theoretical framework of the speech intelligibility index (SII). An adaptive psychophysical procedure, namely the quick-band-importance-function procedure, was developed to enable the fitting of the SII model to individual listeners. Usi...
متن کامل