## 2 Related Works  
This paper aims to find a subword unit suitable for spontaneous speech recognition. Similar to our approach, some studies [13; 14; 15; 16; 17] have attempted to overcome the limitations of the phoneme unit. These studies focused on automatically deriving subword units from speech signals and constructing a lexicon based on them; this was done to build the subword unit using a data-driven, rather than hand-crafted approach. However, most of these studies were performed decades ago and did not focus on improving the performance of spontaneous speech recognizers. In recent years, there has been a renewed interest in handling phoneme-like units for low-resource languages. Thus, most of the previous studies [11; 18; 19; 20; 21; 22] aimed to convert grapheme units to phoneme-like units, rather than focusing on spontaneous speech [9]. Here, the phoneme-like unit is a subword unit, automatically derived from training speech data, not a phoneme unit selected by experts. In general, this subword unit-based speech recognizer uses a lexicon that maps each word to a sequence of subword units, typically phonemes. Here, developing a lexicon [11] for each language requires linguistic knowledge, as well as human effort, which may not be always readily available, particularly for low-resource languages. For this reason, grapheme-based subword units or phoneme-like subword units have been explored in the literature as an alternative to the absence of a phoneme-based lexicon. Phoneme-like units tend to be data-dependent, as they are typically obtained through the optimization of an objective function using training speech data [11]. Moreover, phoneme-like units may facilitate handling pronunciation variations [1]. For this reason (although different in purpose), we can derive a method for building subword units suitable for the spontaneous speech by applying the methods used to build phoneme-like units. To take advantage of existing studies, we compared the pros and cons between the subword unit extension methods, proposed in our previous study [9], and the most recently studied phoneme-like unit discovery method [11]. In the previous study [9], we extended the existing phoneme, set using a large amount of broadcast speech data, in order to build a subword unit suitable for spontaneous speech recognition. This method first extracts variable-length phoneme-level segments from broadcast data and then converts them into fixed-length latent vectors based on a long short-term memory architecture. Then, we used the k-means algorithm to cluster acoustically similar latent vectors to build a new phoneme set by gathering the clustered vectors. In the visualization and clustering experiments, we confirmed that the acoustic information of each segment is better represented by the latent vector, obtained from the LSTM architecture, rather than by the fixed-length feature vector obtained by simple linear interpolation. In the results, the proposed unit is shown to produce better performance than the phoneme-based and grapheme-based units, in both spontaneous and read speech recognition tasks. In a previous study [11], a phoneme-like unit was derived by clustering context-dependent (CD) graphemes in Gaussian mixture model (GMM) and hidden Markov model (HMM) frameworks using maximum likelihood criteria. To do this, CD grapheme-based HMM/GMM systems were trained with 39-dimensional cepstral features extracted using the HTK toolkit [23]. Here, each CD grapheme was modeled with a single HMM state. Finally, the phoneme-like units were derived through likelihood-based decision tree-based clustering using singleton questions [4]. As a result, the phoneme-like unit, which is obtained from clustered CD grapheme units, is shown to significantly yield better performance compared to the grapheme unit in the GMM/HMM system. If we use CD phoneme units instead of CD grapheme units, we can build a unit suitable for spontaneous speech, which is the purpose of this paper.