# Amharic Speech Search Using Text Word Query Based on Automatic Sentence-like Segmentation
## 1 Introduction  
The availability of a vast amount of information stored in audio and video repositories worldwide has increased the interest in searching on speech (SoS) [1]. SoS focuses on retrieving speech content from audio repositories that match user queries, i.e., searching for audio or speech by using any term of interest by text or segment of the audio or voice. Audio data includes everything that can be heard, such as speech, music, animal sounds, bell sounds, laughter, bird chirps, news footage archives, and audio lectures. Pitch is a generally slow-changing periodic signal in spoken speech that corresponds to the frequency of vibration of the vocal cords. Male pitch contribution is often between 50 Hz and 250 Hz,whereas female pitch contribution is typically between 120 Hz and 500 Hz [2]. Audio files can be generated from different sources such as the internet and social media by individuals and organizations. Moreover, recent technological developments in the storage space of machines and their affordable prices make producing and storing audio files less challenging. Now, audio files can be recorded quickly and shared through many channels or platforms to reach the public immediately. These audio files are played using audio player software and social media such as YouTube. However, there is no way to search for a specific word in a speech on these audio players. Speech segmentation is the process of decomposing a long speech signal into a shorter length. Speech segmentation can be classified as manual and automatic. Manual segmentation is a traditional approach in which trained phoneticians segment speech manually. However, this approach is uneven and time-consuming because it relies on hearing and visual interpretation of the necessary boundaries [2]. On the other hand, automatic segmentation segments speech automatically into sentence-like or phrase-like parts. This segmentation is convenient for the design of automatic speech recognition systems. Searching on the speech of an audio file can be done by giving a query term. A query is usually a keyword or a short phrase that is given to the system for retrieving an audio file containing that query. Based on the query type, the searching techniques can be broadly classified into three categories: keyword spotting, spoken term detection (STD), and query-by-example spoken term detection (QBE STD) [3]. Keyword spotting (word spotting) enables to locate a text query within a speech document or a speech stream [4]. In literature, the term keyword spotting has been used as STD. However, according to [PERSON] et al. [4], in keyword spotting, the user query is known in indexing time, whereas in text-based STD the query term is specified at the search time. As a result, STD is more difficult to use since it has no prior knowledge of the queries that are being searched for [5]. Text-based STD is the process of locating a particular search term from a collection of segmented speech [6]. With the increased interest in SoS, STD is a type of SoS that helps to retrieve speech data by using text as a query word that represents a particular speech utterance [7]. The general structure and components of STD systems contain two steps to the STD procedure. The first stage, indexing, creates a database with an intermediate representation of the speech segments stored in a database. Using this intermediate database, the second stage is designed for locating putative occurrences of the query word. The search should be carried out quickly and precisely [5]. The STD has advantages including the possibility of retrieving any speech file that contains any term from its textual representation, allowing for an efficient search of any term in a large index. This technology can be accessed using any device with text input capabilities [3;7]. The state-of-the-art STD systems usually work based on a large vocabulary continuous speech recognition (LVCSR) engine and search for keywords in the results returned by the engine.