def _parseExpectedWords(wordList, defaultSensitivity=80):
    """Parse expected words list.

    This function is used internally by other functions and classes within the
    `transcribe` module.

    Expected words or phrases are usually specified as a list of strings. CMU
    Pocket Sphinx allows for additional 'sensitivity' values for each phrase
    ranging from *0* to *100*. This function will generate to lists, first with
    just words and another with specified sensitivity values. This allows the
    user to specify sensitivity levels which can be ignored if the recognizer
    engine does not support it.

    Parameters
    ----------
    wordList : list of str
        List of words of phrases. Sensitivity levels for each can be specified
        by putting a value at the end of each string separated with a colon `:`.
        For example, ``'hello:80'`` for 80% sensitivity on 'hello'. Values are
        normalized between *0.0* and *1.0* when returned.
    defaultSensitivity : int or float
        Default sensitivity to use if a word does not have one specified between
        0 and 100%.

    Returns
    -------
    tuple
        Returns list of expected words and list of normalized sensitivities for
        each.

    Examples
    --------
    Specifying expected words to CMU Pocket Sphinx::

        words = [('hello:95', 'bye:50')]
        expectedWords = zip(_parseExpectedWords(words))

    """
    defaultSensitivity = defaultSensitivity / 100.  # normalized

    sensitivities = []
    if wordList is not None:
        # sensitivity specified as `word:80`
        wordListTemp = []
        for word in wordList:
            wordAndSense = word.split(':')
            if len(wordAndSense) == 2:  # specified as `word:80`
                word, sensitivity = wordAndSense
                sensitivity = int(sensitivity) / 100.
            else:
                word = wordAndSense[0]
                sensitivity = defaultSensitivity  # default is 80% confidence

            wordListTemp.append(word)
            sensitivities.append(sensitivity)

        wordList = wordListTemp

    return wordList, sensitivities