### Proposed Method and Its Contribution  
This paper presents an online multichannel ASR system employing a BLSTM AM, which is hardly deployed in industries even though it is one of the best performing AMs. Accordingly, our online ASR system is based on the server-client model where the server can perform speech recognition using high-performance resources and the client interacts with the user and server via various devices. For the baseline server-side ASR system, we utilize a CSC BLSTM where the sizes of a chunk and its left and right contexts of a CSC are 20, 40, and 40 ms, respectively [30]. In fact, the baseline ASR performs in real time; however, the number of concurrent clients is limited. For more concurrent clients, we propose a method for accelerating the GPU parallelization and reducing the transmission overhead between the CPU and GPU based on the fact that a CSC is regarded as an isolated sequence. Moreover, we propose a DNN-based voice activity detector (DNN-VAD) for a low-latency response even though the client sends no end-of-utterance message. To evaluate the proposed online ASR system, we use the test data recorded from Korean documentary programs. The performance in terms of ASR accuracy, real time, and latency is measured as a function of the syllable error rate (SyllER), number of concurrent clients, and the elapsed time in seconds until a user receives the recognized text of they spoke. The contributions of this paper are summarized as follows:  
**Fast BLSTM-based online ASR system** A BLSTM based AM is commonly regarded as an offline ASR system since a vanilla BLSTM can be decoded after an overall input speech is obtained. This paper successfully deploys a CTC-BLSTM-AM-based online ASR by using the proposed multichannel parallel acoustic score computation and DNN-VAD methods. It is noted that the proposed system can be employed for any language if proper acoustic and language models are prepared for the language though our experiments are conducted only for Korean in this paper. Parallel acoustic score computation using multichannel dataEven though a BLSTM-based online ASR system is successfully deployed using CTC-BLSTM, the number of concurrent clients is still limited due to the massive computational complexity. The proposed acoustic score computation method increases the number of concurrent clients by merging multichannel data and by processing the data in parallel, which accelerates the GPU parallelization and reduces the data transfer between CPU and GPU. From the ASR experiments, the number of concurrent clients is increased from 22 to 44 by using the proposed parallel acoustic score computation method. **DNN-based voice-activity detector** An online ASR system needs to send the recognized text to a user as soon as possible even before the end of sentence is detected to reduce response time. To this end, we propose a DNN-based voice-activity detector that detects short pauses in a continuous utterance. The benefits of the proposed method are (a) to reduce user perceived latency from 11.7 s to 5.41 s and 3.09 s respectively depending on the parameter with little degradation in accuray and (b) this is done by the use of the acoustic model score used in ASR without any auxiliary training or the additional computation while decoding. **ASR performance** The proposed method maintains the accuracy performance whereas many previous works degrade ASR performance. **Combination with additional optimization** An additional optimization method that causes performance degradation can be applied to the proposed ASR. For instance, we utilize a frame skip method during Viterbi decoding to our ASR system. From the ASR experiments, the number of concurrent clients is increased from 44 to 59 while the syllable error rate is degraded from 11.94 to 12.53%. The rest of this paper is organized as follows. Section 2 describes our configuration of a server-client-based ASR system, an acoustic score computation CTC BLSTM AM, and a baseline multichannel acoustic score computation using a CSC BLSTM AM.