Today, cochlear implants (CIs) are the treatment of choice in patients with profound hearing loss. However speech intelligibility with these devices is still limited. A factor that determines hearing performance is the processing method used in CIs. Therefore, research is focused on designing different speech processing methods. The evaluation of these strategies is subject to variability as it is usually performed with cochlear implant recipients. Hence, an objective method for the evaluation would give more robustness compared to the tests performed with CI patients.
This paper proposes a method to evaluate signal processing strategies for CIs based on a hidden markov model speech recognizer. Two signal processing strategies for CIs, the Advanced Combinational Encoder (ACE) and the Psychoacoustic Advanced Combinational Encoder (PACE), have been compared in a phoneme recognition task. Results show that PACE obtained higher recognition scores than ACE as found with CI recipients.
Bibliographic reference. Nogueira, Waldo / Harczos, Tamás / Edler, Bernd / Ostermann, Jörn / Büchner, Andreas (2007): "Automatic speech recognition with a cochlear implant front-end", In INTERSPEECH-2007, 2537-2540.