  In recent years, End-to-End speech recognition technology based on deep
learning has developed rapidly. Due to the lack of Turkish speech data, the
performance of Turkish speech recognition system is poor. Firstly, this paper
studies a series of speech recognition tuning technologies. The results show
that the performance of the model is the best when the data enhancement
technology combining speed perturbation with noise addition is adopted and the
beam search width is set to 16. Secondly, to maximize the use of effective
feature information and improve the accuracy of feature extraction, this paper
proposes a new feature extractor LSPC. LSPC and LiGRU network are combined to
form a shared encoder structure, and model compression is realized. The results
show that the performance of LSPC is better than MSPC and VGGnet when only
using Fbank features, and the WER is improved by 1.01% and 2.53% respectively.
Finally, based on the above two points, a new multi-feature fusion network is
proposed as the main structure of the encoder. The results show that the WER of
the proposed feature fusion network based on LSPC is improved by 0.82% and
1.94% again compared with the single feature (Fbank feature and Spectrogram
feature) extraction using LSPC. Our model achieves performance comparable to
that of advanced End-to-End models.
