@article{Zirn2017, author = {Stefan Zirn}, title = {Development of a home-based auditory training to improve speech recognition on the telephone for patients with cochlear implants: A randomised trial}, series = {Clinical Otolaryngology}, volume = {42}, number = {6}, issn = {1749-4486}, doi = {10.1111/coa.12871}, pages = {1303 -- 1310}, year = {2017}, abstract = {Objectives: Speech recognition on the telephone poses a challenge for patients with cochlear implants (CIs) due to a reduced bandwidth of transmission. This trial evaluates a home-based auditory training with telephone-specific filtered speech material to improve sentence recognition. Design: Randomised controlled parallel double-blind. Setting: One tertiary referral centre. Participants: A total of 20 postlingually deafened patients with CIs. Main outcome measures: Primary outcome measure was sentence recognition assessed by a modified version of the Oldenburg Sentence Test filtered to the telephone bandwidth of 0.3-3.4 kHz. Additionally, pure tone thresholds, recognition of monosyllables and subjective hearing benefit were acquired at two separate visits before and after a home-based training period of 10-14 weeks. For training, patients received a CD with speech material, either unmodified for the unfiltered training group or filtered to the telephone bandwidth in the filtered group. Results: Patients in the unfiltered training group achieved an average sentence recognition score of 70.0\%±13.6\% (mean±SD) before and 73.6\%±16.5\% after training. Patients in the filtered training group achieved 70.7\%±13.8\% and 78.9\%±7.0\%, a statistically significant difference (P=.034, t10 =2.292; two-way RM ANOVA/Bonferroni). An increase in the recognition of monosyllabic words was noted in both groups. The subjective benefit was positive for filtered and negative for unfiltered training. Conclusions: Auditory training with specifically filtered speech material provided an improvement in sentence recognition on the telephone compared to training with unfiltered material.}, language = {en} }