This paper presents a comparison of a traditional hybrid speech recognition system (kaldi using WFST and TDNN with lattice-free MMI) and a lexicon-free end-to-end (TensorFlow implementation of multi-layer LSTM with CTC training) models for German syllable recognition on the V erbmobil corpus. The results show that explicitly modeling prior knowledge is still valuable in building recognition systems. With a strong language model (LM) based on syllables, the structured approach significantly outperforms the end-to-end model. The best word error rate (WER) regarding syllables was achieved using kaldi with a 4-gram LM, modeling all syllables observed in the training set. It achieved 10.0% WER w.r.t. the syllables, compared to the end-to-end approach where the best WER was 27.53%. The work presented here has implications for building future recognition systems that operate independent of a large vocabulary, as typically used in a tasks such as recognition of syllabic or agglutinative languages, out-of- vocabulary techniques, keyword search indexing and medical speech processing.
Computerized face recognition is seen by many analysts as the optimal means to prevent unauthorized access to computer systems. Facial recognition also has other applications, like improving social networks and the curating of photographs for news media. To be efficient systems need to enable a computer to estimate with precision a person's age based on the analysis of their face. The new advancement comes from the Department of Electronics and Telecommunication Engineering, at the Shri Guru Gobind Singhji Institute of Engineering and Technology, in Vishnupuri, Nanded, India. The researchers suggest that age classification will add a tighter aspect to security systems and surveillance.
Facial recognition (FR) technology has come a long way in recent years in terms of applicability. However, the standard FR deploy still presents several difficulties with it. They may range from the method accuracy and performance, requirement of specific setups to ease integration and mobile support. With the latest release of our facial recognition API (frAPI) version 5.0 we aim to addressed all those problems together such that our clients can be up and running their FR system within fifteen minutes. The facial recognition, as well as other Computer Vision areas, had a recent breakthrough with the use of Deep Learning.