# Speech Recognition for Task Domains with Sparse Matched Training Data
## 4 Experimental Settings
### Corpus Descriptions  
In order to verify the proposed methods, ASR for non-native Korean speech and Korean call center recording were used as the main task domains with sparse matched training data. The Korean broadcast speech corpus was used for the large-scale speech DB. The AMI meeting corpus [37] and certain other sources were used for the functional verification of the integrated systems. Table 1 shows the summarized characteristics of the corpora used for the experiments.  
* AMI Meeting corpus ([[http://groups.inf.ed.ac.uk/ami/corpus](http://groups.inf.ed.ac.uk/ami/corpus)]([http://groups.inf.ed.ac.uk/ami/corpus](http://groups.inf.ed.ac.uk/ami/corpus)))  
The AMI meeting corpus consists of 100 h of annotated recordings of planned meetings. For each meeting, four participants have a free conversation in English, and simulate a project meeting on product design. The meetings last approximately 30 min each, and multiple microphones are used to simultaneously record conversations in different environments. Of the available microphone channels, we used the individual headset microphone (IHM) channel for clean close-talking speech and the single distant microphone (SDM) channel for far-field noisy speech.  
* Non-native Korean speech corpus  
We used 520 h of the in-house non-native Korean speech corpus gathered from Korean language education providers. This corpus was spoken by 830 non-native Korean speakers and recorded via PC microphone and smartphone channels. This non-native Korean speech corpus has been collected for approximately five years from Korean language education providers using speech technology for non-native speakers residing in Korea [38]. The level of Korean for each non-native speaker varies from beginner to advanced and all speech data was transcribed by humans.  
* Korean call center recording corpus  
The Korean call center recording corpus used in this study contains approximately 1000 h of conversations recorded at operating call centers. This information was provided with personal information deleted [39]. It is mainly composed of conversations between agents and customers. The speech recognition performance of the database is degraded due to its acoustic characteristics including the overall fast speaking rate and inaccurate pronunciation. Therefore, a considerable amount of data is required to improve the performance, but it represents a domain in which training data collection is difficult due to problems such as privacy protection.  
* Korean broadcast speech corpus  
The Korean broadcast audio speech corpus was used for the large-scale speech DB for the general domain as an opposite to the domain with sparse matched data. The broadcast audio data was  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Corpus** & **Description of Purpose** & **Duration (h)** & **Number of Speakers** \\ \hline AMI Meeting &
\begin{tabular}{c} Functional verification of the \\ integrated systems \\ \end{tabular} & 100 & 200 \\ Non-native Korean & Task domain with the sparse & & \\ speech & matched training DB & 520 & 830 \\ Korean call center & Task domain with the sparse & & \\ recording & matched training DB & & \\ Korean broadcast speech & Large-scale labeled speech DB & 14,000 & \(>1000\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summarization of the corpora used for the experiments.  
easily accessible in large amounts. It contains speech data uttered by various speakers in diverse noise environments; therefore, it could be used to build a large-scale speech DB for improving ASR performance in the general domain. We used 14,000 h of the Korean broadcast speech corpus, with reliable transcriptions extracted from multi-genre broadcast raw audio data with inaccurate subtitle timestamps through the method proposed in [40]. In this study, all speech data was down-sampled to 8 kHz, the sampling rate of the Korean call center recording corpus.