# Methodology for Obtaining High-Quality Speech Corpora
## 2 Background  
Methodological approaches to the creation of speech corpora have been published in several papers. In [10], an exhaustive general description of the methodology of collecting data for speech corpora is provided, covering specifications and various technical and even legal aspects. In [11], examples of check lists are provided that can be applied to assure good quality of the created corpora. A corpus creation pipeline is presented in [12], which describes the process of creating a large corpus. The procedures of creating speech corpora are also published as part of papers describing the creation of specific corpora. For example, the methodology applied in the process of creating a corpus for speech translation is described in [13]. In [9], a methodology is presented for creating a domain-specific corpus. The process of creating a speech corpus based on a web questionnaire is presented in [14]. There are also papers describing particular facets of speech corpora, e.g., data tagging, representativeness, and tools for speech corpora; more details are given below. A speech corpus, or spoken corpus, is a collection of spoken language data (often of large size) that may include audio recordings and transcriptions. There are many speech corpora of various sizes for various languages, e.g., for English, GigaSpeech [12] is used, which contains 10,000 h of tagged audio data and 40,000 h of audio recordings; there are also speech corpora for many other languages [15], including low-resource languages [16]. Speech corpora can include audiovisual data, but audiovisual corpora are rather small [17, 18, 19]; for example, the corpus described in [17] includes speech from 35 speakers. Currently, speech data are often collected remotely via smartphones (previously also any phones) or other mobile devices, or via applications or web browser platforms that allow voice recording and crowdsourcing [20,21]. The recording sessions are based on predefined scenarios and promoters displaying texts, which can be subsequently used in ASR training, even though the speakers may not follow them accurately. Although these recordings are not acquired under controlled conditions, as speech is recorded in various environments rather than in the studio, good quality can still be maintained if signals of insufficient quality are identified and replaced or removed [22]. Since speech corpora are databases, the quality criteria applied to databases can also be applied to speech corpora. Therefore, the data in any speech corpus should be accessible, accurate, complete, consistent, up to date, useful, valid, etc. The details can be found in numerous sources (see [23]). An analysis of the collected data can help to identify possible defects, determine their causes, and determine how to avoid them. Low-quality data generate additional costs, which are necessary to correct these data and improve their quality. In the case of speech, its quality can also be described in terms of signal quality, i.e., the signal-to-noise ratio, or possible distortions. Nevertheless, various noises can be intentionally added to speech data (to clean speech, often already labeled, to avoid labeling problems) when training ASR algorithms to improve their generalizability [24,25] or for various tests. Speech corpora are often tagged with linguistic information, i.e., annotated. Annotation includes the addition of part-of-speech (POS) tags, morphosyntactic descriptors, lemmatization, syntactic parsing, phonetic transcription, orthographic transcription, semantic annotation, prosodic annotation, stylistic annotation, pragmatic annotation, discourse annotation, sentiment annotation, etc. [26; 27; 28; 29]. To create richly annotated corpora, the data (both text and audio signals) can be annotated manually, or automated tools can be used to reduce annotation time [30; 31]. The annotation framework has been standardized by ISO [32]. There are many types of speech corpora. For example, corpora may contain spoken or written language.