## 5 Experiment
### Corpus and Baseline Korean ASR  
We use the 3440-h Korean speech and its transcription data to train the baseline Korean ASR system. The speech data comprise approximately 19-million utterances, which are recorded in various environments, such as speaker, noise environment, recording device, and recording scripts. Each utterance is sampled at a rate of 16 kHz and no further augmentation methods are adopted. To evaluate the proposed methods, we prepare a test set recorded from documentary programs. The recordings include voices of narrators and interviewee with and without various background music and noises. The recordings are manually split into 69 segments, each of which are 29.24-s long, on average, and 33.63 min in total. Each utterance of the training speech data is converted into 600-dimensional speech features. With the extracted speech features, a CSC BLSTM AM is trained using a Kaldi toolkit [40], where the chunk and context sizes of the CSC are 20 and 40 ms, respectively. The AM comprises one input layer, five BLSTM layers, a fully connected layer, and a soft-max layer. Each BLSTM layer comprises 640 BLSTM cells and 128 projection units, while the output layer comprises 19901 units. For the language model, 38 GB of Korean text data is first preprocessed using text-normalization and word segmentation methods [41], and then, the most frequent 540k sub-words are obtained from the text data (For Korean, a sub-word unit is commonly used as a basic unit of an ASR system [42; 43]). Next, we train a back-off trigram of 540k sub-words [41] using an SRILM toolkit [44; 45]. During decoding, the minibatch size is set to 2 s. Although a larger minibatch size increases the decoding speed owing to the bulk computation of GPU, the latency also increases. We settle into 2 s of minibatch size as a compromise between decoding speed and latency [14; 26; 36]. To compare the baseline ASR system, we additionally train two types of AMs--(a) DNN-based AM and (b) LSTM-based AM. A DNN-based AM comprises one input layer, eight fully connected hidden layers, and a soft-max layer. Each hidden layer comprises 2048 units and the output layer comprises 19901 units. An LSTM-based AM consists of one input layer, five LSTM layers, a fully connected layer, and a soft-max layer. Each LSTM layer consists of 1024 LSTM cells and 128 projection units, and the output layer consists of 19901 units. The ASR accuracy performance is measured using SyllER, which is calculated as following \[SyllER=\frac{S+D+I}{N}\times 100, \tag{14}\]  
where \(S\), \(D\), \(I\), and \(N\) are the numbers of substuted syllables, deleted syllables, inserted syllables, and reference syllables, respectively. As shown in Table 5, the BLSTM AM achieves an error rate reduction (ERR) of 20.66% for the test set when compared to the DNN-based AM. In addition, it achieves an ERR of 11.56% for the test set when compared to the LSTM-based AM. Therefore, we employ the BLSTM AM as our baseline ASR system for achieving better ASR accuracy performance (As for the comparision, the Korean speech recognition experiments with the same data set using the Google Cloud API achieved an average SylIER of 14.69%). Next, we evaluate the multichannel performance of ASR systems employing the three AMs by examining the maximum number of concurrent clients where an ASR system can be performed in real time. That is, multiple clients are parallelly connected to an ASR server and each client requests to decode the test set. We then measure the real time factor for each client using the following equation  
\[\text{RTF}_{i}=\frac{\text{The processing time for the client }i}{\text{The total duration of the test set}}. \tag{15}\]  
Next, we confirm that the number of concurrent clients are performed in real time if the average real-time factors for the concurrent clients are smaller than 1.0. As shown in Table 6, the BLSTM AM supports 22 concurrent clients for the test set, whereas the DNN- or LSTM- based AMs support more concurrent clients.