# Methodology for Obtaining High-Quality Speech Corpora
## 2 Background
A static corpus does not change over time, whereas a dynamic corpus (also called a monitor corpus) is ever growing to illustrate changes in language over time; these corpora are often updated from online data sources. A synchronic corpus stores language data coming from a given point in time, while a diachronic (historical) corpus stores language data representing various time periods. A monolingual corpus contains data for one language only, whereas a multilingual corpus has data for more than one language; a bilingual corpus involves two languages. In particular, parallel corpora contain source texts and their translations into selected languages, and comparable corpora are nonparallel (or less parallel), i.e., their sub-corpora have been built according to the same criteria and using the same principles of data selection [33; 34; 35]. A general corpus should provide wide coverage of the language and be possibly balanced with respect to genres and the domains of the language, whereas a specialized corpus represents a sublanguage (possibly jargon), e.g., a specific domain or genre. A reference corpus (see, for example, [36; 37]) is a benchmark corpus \"designed to provide comprehensive information about language\" [33], and an opportunistic corpus contains the material collected using an opportunistic strategy, i.e., exploiting opportunities to obtain the corpus material. The main corpus types are summarized in Table 1.  
Speech corpora may contain both spontaneous and read/prepared speech. Read speech may include news, book excerpts, numbers, word lists, and syllables, possibly both as text and audio files. In this case, text files include at least orthographic transcription (i.e., read texts) and possibly phonetic transcription; these data may be aligned with audio files. Spontaneous speech may include monologs, dialogs, and recordings of more people speaking, sometimes simultaneously, with hesitations, disfluencies, self-repairs, incomprehensible utterances, etc. These corpora may also include dialects. There are tools for the automated annotation of speech corpora [38], but manual work is still needed, especially in the case of dialects [39]. The data collected for any corpus should be representative of the language, according to the criteria chosen for each corpus. Representativeness is difficult to assure, and this issue has been discussed broadly in the literature, even decades ago [33; 40; 41; 42]. Since it is not feasible to collect total language production, sampling is necessary. Sampling approaches follow various criteria and include random sampling, collecting the same amount of data for each sub-corpus, reflecting spoken/written language production in each category, etc. The sampling frame should cover a wide range of varieties of the language to assure \begin{table}
\begin{tabular}{c c} \hline
**Criterion** & **Basic Corpus Types** \\ \hline Annotation & Annotated, unannotated \\ \hline Language coverage & Reference, opportunistic \\  & General, specialized \\ \hline Changes & Static, dynamic \\ \hline Time period & Synchronic, diachronic \\ \hline Speech type & Read speech, spontaneous speech \\ \hline Number of languages & Monolingual, multilingual (parallel, comparable) \\ \hline \end{tabular}
\end{table} 
Table 1: Main corpus types. diversity, and the data should be balanced; thus, the subsamples for various language varieties should be proportional to their importance in the language. To obtain a representative and balanced corpus, the purpose of creating a given corpus should be defined first, as should the target user. Next, pilot studies are recommended, and if needed, the corpus structure should be revised. All steps taken should be documented, and information about the decisions made and on the content of the corpus should be available to users. The collected data should, if possible, include complete speech events, e.g., conversations.