2018/10/05 by Oscar Koller, Sepehr Zargaran, Hermann Ney +1 · 1 citation
Computer Science · Engineering · Psychology · #Gait Recognition and Analysis #Hand Gesture Recognition Systems #Hearing Impairment and Communication
paper · pdf · doi:10.1007/s11263-018-1121-3
openalex publication_date 2018/10/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/31
This manuscript introduces the end-to-end embedding of a CNN into a HMM, while interpreting the outputs of the CNN in a Bayesian framework. The hybrid CNN-HMM combines the strong discriminative abilities of CNNs with the sequence modelling capabilities of HMMs. Most current approaches in the field of gesture and sign language recognition disregard the necessity of dealing with sequence data both for training and evaluation. With our presented end-to-end embedding we are able to improve over the state-of-the-art on three challenging benchmark continuous sign language recognition tasks by between 15 and 38% relative reduction in word error rate and up to 20% absolute. We analyse the effect of the CNN structure, network pretraining and number of hidden states. We compare the hybrid modelling to a tandem approach and evaluate the gain of model combination.