# Methodology for Obtaining High-Quality Speech Corpora
## 4 Assessing the Quality of Speech Corpora
### Representativeness  
A representative corpus should reflect the distribution of linguistic phenomena (words, grammatical structures, etc.) in the language stored in this corpus [75]. Corpus representativeness depends on the goal of creating the corpus, and, as [PERSON] et al. [76] state, \"a corpus may be representative to a certain extent, in particular ways, and for particular purposes\". [PERSON] describes representativeness as the extent to which a sample includes the full range of variability in a population [40]. Since corpus construction requires sampling of the language, a representative corpus should be balanced, i.e., the proportion of sampled elements should reflect their frequency in the target language [42]. A representative corpus usually includes a wide range of language varieties, sampled in proportion to their frequencies and/or weights in the sampled target population. There are many language varieties, classified according to various criteria, which may include general language, written and spoken language, dialects and subdialects, regional language, colloquial language, sociolect, literary language, officialese, journalese, artlang, scientific writing, etc. The language varieties covered in the corpus should be indicated in the corpus description. Domains assumed to be included in the corpus determine what thematic categories should be covered by this corpus. The list of corpus domains should be provided in the corpus description. It is implicitly assumed that a corpus will balance itself when it reaches a substantial size. Larger corpora are also needed if low-frequency words or word cooccurrences are of interest to the users [77]. Large speech corpora have been built quite easily in recent years, often using mobile devices [78]. Therefore, achieving a large total number of speakers or hours of recordings can be assumed when creating speech corpora. Corpus size is often described by the number of recorded hours of speech. However, the recordings usually also include silence and non-speech audio material, so the actual speech time is less than the total recording time. Another indicator of corpus size is the number of sentences in total or per speaker. The corpus size can also be specified by the storage size, e.g., in gigabytes. The evaluation of corpus representativeness depends on whether the full population can be analyzed. According to [79], there are two types of corpus representativeness: target domain representativeness, which describes \"the extent to which a corpus contains the full range of text type variability that exists in the target domain\", and linguistic representativeness, which \"determines the suitability of a corpus sample for answering specific research questions about specific linguistic features\". If the full target population is not known and cannot be analyzed, then target domain representativeness can be extrapolated from the detailed analysis of available data, and linguistic representativeness can be assessed based on randomly selected samples from the corpus. If the full target population is known, then both the target domain and linguistic representativeness can be assessed based on the comparison of occurrences and proportions of domain and linguistic features in the corpus to the full population. A representative, balanced corpus should reflect average language use. Therefore, [PERSON] [80] measured representativeness by comparing word familiarity ratings, word association, and word relatedness (collected from test persons) with word frequency, word co-occurrence, and contextual statistics computed from a corpus. Various co-occurrence measures can be applied; see [81]. In [PERSON] [80], contextual statistics were calculated by counting word co-occurrences, applying an association measure (log likelihood ratio, or simpler, e.g., \(\log(n_{ij}+1)\), where \(n_{ij}\) is the number of co-occurrences of words \(i\), \(j\)) to the raw counts and then computing vector similarities using a cosine measure.