# Amharic Speech Search Using Text Word Query Based on Automatic Sentence-like Segmentation
## 1 Introduction
The search for out-of-vocabulary (OOV) words remains challenging since the LVCSR engine always misrecognizes the OOV words [8]. The OOV refers to words that are not in the lexicon and is the most common source of error in ASR [9;10]. Different approaches are available to minimize the OOV effect on the ASR. One way of achieving high lexical coverage is by building a language model (LM) on the morpheme level [11]. On the other hand, QBE STD is a technique in which a user presents the system with the desired audio snippets containing queries. The system then searches the database for segments that closely resemble the query [3]. ASR allows the machine to understand the user's speech and convert it into a series of words through a computer program; thereby creating a kind of natural communication between human and machine [12]. The ASR components include acoustic front-end, acoustic model, lexicon, LM, and decoder. The acoustic front-end converts the speech signal into appropriate features used by the recognizer. The process of converting the audio wave form into a sequence of fixed-size acoustic vectors is called feature extraction. Feature vectors are generally generated every 10 milliseconds using a 25 millisecond overlapping analysis frame. The decoder searches through all possible word sequences to find the sequence of words most likely to generate. The LM is generally an n-gram model where each \(n\) word'slikelihood is solely dependent on its \(n-1\) predecessors [13]. Moreover, smoothing is a technique essential in constructing the n-gram LM, a main part of speech recognition. It is a set of procedures for fine-tuning the maximum likelihood estimation (MLE) by counting events in the training corpus to produce more accurate probabilities. Some of the smoothing techniques, such as Laplace Smoothing, Add \(\lambda\) Smoothing, Natural Discounting, Good-Turing Smoothing, Interpolation, and Backoff can solve the problem of data sparsity based on the raw frequency of n-grams. The details of each smoothing technique are clearly elaborated by [PERSON] [14]. The author used interpolation as a smoothing technique and solved the problem of data sparsity through n-gram hierarchy. Furthermore, the probability estimates of all n-gram orders were combined based on the assumption that, if there is not enough data to estimate a probability in the higher-order n-gram, the lower-order n-gram can frequently give relevant information [14]. To date, around 7000 languages are spoken in the world [15]. Amharic is the official working language of the government of Ethiopia, an East African country with a population of over 100 million. It is one of the Ethio-Semitic languages, which belongs to the Semitic branch of the Afro-Asiatic family and has the second largest number of speakers in the world after Arabic [16] and is the most widely spoken Semitic language in Ethiopia [17]. The majority of the speakers of Amharic can be found in Ethiopia; however, there are some speakers in other nations, such as Israel, Eritrea, Canada, the USA, and Sweden [9; 18]. Audio data are abundantly found in Amharic language via the various private, social, and government media platforms. The high prevalence of social media and multimedia in our interconnected global society today has created the need to access different audio files. Individuals and organizations use this audio file to satisfy their information needs. Locating a spoken word in an audio file, however, is a challenge, because users may know that the speaker spoke a word but may not know in which part of the audio file that the word was spoken. For instance, to find a spoken word \(\mathbf{\lambda}\).\(\mathbf{\tau}\).\(\mathbf{\tau}\).\(\mathbf{\tau}\).\(\  
* We prepared the speech corpus and conducted an extensive experimental evaluation to check the performance of the proposed work. The rest of the paper is described as follows: Section 2 presents a discussion of the Amharic language and Section 3 elaborates the related works. Section 4 describes the materials and methods.