A novel speech-processing strategy incorporating tonal information for cochlear implants.
Good performance in cochlear implant users depends in large part on the ability of a speech processor to effectively decompose speech signals into multiple channels of narrow-band electrical pulses for stimulation of the auditory nerve. Speech processors that extract only envelopes of the narrow-band signals (e.g., the continuous interleaved sampling (CIS) processor) may not provide sufficient information to encode the tonal cues in languages such as Chinese. To improve the performance in cochlear implant users who speak tonal language, we proposed and developed a novel speech-processing strategy, which extracted both the envelopes of the narrow-band signals and the fundamental frequency (F0) of the speech signal, and used them to modulate both the amplitude and the frequency of the electrical pulses delivered to stimulation electrodes. We developed an algorithm to extract the fundatmental frequency and identified the general patterns of pitch variations of four typical tones in Chinese speech. The effectiveness of the extraction algorithm was verified with an artificial neural network that recognized the tonal patterns from the extracted F0 information. We then compared the novel strategy with the envelope-extraction CIS strategy in human subjects with normal hearing. The novel strategy produced significant improvement in perception of Chinese tones, phrases, and sentences. This novel processor with dynamic modulation of both frequency and amplitude is encouraging for the design of a cochlear implant device for sensorineurally deaf patients who speak tonal languages.
Lan
N
N
Department of Biokinesiology and Physical Therapy, University of Soutern California, CHP-155, Los Angeles, CA 90089, USA. ninglan@usc.edu
Nie
K B
KB
Gao
S K
SK
Zeng
F G
FG
D000465:Algorithms; D002681:China; D003054:Cochlear Implants; D019544:Equipment Failure Analysis; D006309:Hearing / Q000502:physiology*; D006801:Humans; D007802:Language; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated; D010897:Pitch Discrimination / Q000502:physiology*; D011474:Prosthesis Design; D012815:Signal Processing, Computer-Assisted; D013065:Speech Intelligibility / Q000502:physiology*; D013067:Speech Perception / Q000502:physiology*